CONVOLUTIONAL NEURAL NETWORKS FOR DETECTION OF MALFORMATIONS OF CORTICAL DEVELOPMENT¶
Import packages and functions¶
In [1]:
import matplotlib as mpl
%matplotlib inline
from PIL import Image
import numpy as np
import pandas as pd
import os
from skimage.color import gray2rgb
import matplotlib.pyplot as plt
import matplotlib.cm as cm
from mpl_toolkits.axes_grid1 import ImageGrid
from sklearn.utils import shuffle
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import activations
from tensorflow.keras.preprocessing import image
from tensorflow.keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Input, concatenate, Dense, Dropout, Activation, Flatten, GaussianNoise, BatchNormalization, GlobalAveragePooling2D, Conv2D, MaxPooling2D
from tensorflow.keras.optimizers import Adam, RMSprop
from tensorflow.keras.applications.inception_v3 import InceptionV3
from tensorflow.keras.applications.resnet50 import ResNet50
from tensorflow.keras.applications.inception_resnet_v2 import InceptionResNetV2
from tensorflow.keras.models import Model
from sklearn.metrics import confusion_matrix
from sklearn.metrics import accuracy_score
from sklearn.metrics import roc_auc_score
from tensorflow.keras.models import model_from_json
from tensorflow.keras import backend as K
from tensorflow.keras.utils import to_categorical
from tf_keras_vis.gradcam import Gradcam
from tf_keras_vis.saliency import Saliency
from tf_keras_vis.utils import normalize
from sklearn.metrics import classification_report
In [2]:
# Define image size
mpl.rcParams['figure.figsize'] = (20,24)
FIRST PART: DATA INGESTION AND DATA AUGMENTATION¶
Data description¶
We have trained our CNNs with a training set of:
-369 normal MRI images from 19 control patients
-401 MRI images of diffuse malformations of cortical development from 27 patients
-389 MRI images of periventricular nodular heterotopia (PVNH) from 21 patients
And a validation set of:
-159 normal MRI images from 8 control patients
-147 MRI images of diffuse malformations of cortical development from 10 patients
-175 MRI images of periventricular nodular heterotopia (PVNH) from 4 patients
Import original images¶
In [3]:
# Unzip files
!unzip ~/data/Controltrain.zip -d ~/data/
!unzip ~/data/Controlval.zip -d ~/data/
!unzip ~/data/CMtrain.zip -d ~/data/
!unzip ~/data/CMval.zip -d ~/data/
!unzip ~/data/PVNHtrain.zip -d ~/data/
!unzip ~/data/PVNHval.zip -d ~/data/
# Remove the zipped files
!rm ~/data/Controltrain.zip
!rm ~/data/Controlval.zip
!rm ~/data/CMtrain.zip
!rm ~/data/CMval.zip
!rm ~/data/PVNHtrain.zip
!rm ~/data/PVNHval.zip
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Paths to original and processed images¶
In [4]:
# Path to the folder with the original images
pathtoimagesControltrain = './data/Controltrain/'
pathtoimagesControlval = './data/Controlval/'
pathtoimagesCMtrain = './data/CMtrain/'
pathtoimagesCMval = './data/CMval/'
pathtoimagesPVNHtrain = './data/PVNHtrain/'
pathtoimagesPVNHval = './data/PVNHval/'
# Create directories to save the processed images
! mkdir ~/data/processedControltrain
! mkdir ~/data/processedControlval
! mkdir ~/data/processedCMtrain
! mkdir ~/data/processedCMval
! mkdir ~/data/processedPVNHtrain
! mkdir ~/data/processedPVNHval
# Path to the folder with the processed images
pathtoprocessedimagesControltrain = './data/processedControltrain/'
pathtoprocessedimagesControlval = './data/processedControlval/'
pathtoprocessedimagesCMtrain = './data/processedCMtrain/'
pathtoprocessedimagesCMval = './data/processedCMval/'
pathtoprocessedimagesPVNHtrain = './data/processedPVNHtrain/'
pathtoprocessedimagesPVNHval = './data/processedPVNHval/'
# Create directories to save the augmented images for the train datasets
! mkdir ~/data/augmentedControltrain
! mkdir ~/data/augmentedCMtrain
! mkdir ~/data/augmentedPVNHtrain
# Create the directory to save the augmented images
pathtoaugmentedimagesControltrain = './data/augmentedControltrain/'
pathtoaugmentedimagesCMtrain = './data/augmentedCMtrain/'
pathtoaugmentedimagesPVNHtrain = './data/augmentedPVNHtrain/'
Read in, preprocess, and augment Controltrain images¶
In [5]:
# Define the image size
image_size = (512, 512)
# Read in the training images
Controltrain_dir = pathtoimagesControltrain
Controltrain_files = os.listdir(Controltrain_dir)
# For each image
for f in Controltrain_files:
# Open the image
img = Image.open(Controltrain_dir + f)
# Resize the image so that it has a size 512x512
img = img.resize(image_size)
# Transform into a numpy array with no page number and save it into the preprocessed folder
img_arr = np.array(img)
img_arr[462:512, 0:100, :] = np.mean(img_arr[452:462, 0:100, :])
processed_img = Image.fromarray(img_arr, 'RGB')
processed_img_name = './data/processedControltrain/'+'processed'+str(np.random.randint(low=1, high=1e8))+ \
str(np.random.randint(low=1e4, high=1e6))+str(np.random.randint(low=1e4, high=1e6))+ \
str(np.random.randint(low=1e4, high=1e6))+str(np.random.randint(low=1e5, high=1e8))+ \
str(np.random.randint(low=1e2, high=1e7))+str(np.random.randint(low=1e3, high=1e5))+ \
str(np.random.randint(low=1e2, high=1e8))+'.jpg'
processed_img.save(processed_img_name)
In [6]:
# Define the characteristics for data augmentation
datagen = ImageDataGenerator(
rotation_range=25,
width_shift_range=0.15,
height_shift_range=0.15,
shear_range=0.15,
zoom_range=0.25,
horizontal_flip=True,
fill_mode='nearest')
# Path to images
ProcessedControltrain_files = os.listdir(pathtoprocessedimagesControltrain)
# Augment the images
ProcessedControltrain_dir = pathtoprocessedimagesControltrain
for f in ProcessedControltrain_files:
img = load_img(ProcessedControltrain_dir + f)
x = img_to_array(img)
x = x.reshape((1,) + x.shape)
# Save the augmented images into a directory of augmented images
i = 0
for batch in datagen.flow(x, batch_size=1,
save_to_dir=pathtoaugmentedimagesControltrain,
save_prefix='augmented'+str(np.random.randint(low=1, high=1e8))+str(np.random.randint(low=1e4, high=1e6))+str(np.random.randint(low=1e4, high=1e6))+str(np.random.randint(low=1e4, high=1e6))+
str(np.random.randint(low=1e5, high=1e8))+str(np.random.randint(low=1e2, high=1e7))+str(np.random.randint(low=1e3, high=1e5))+str(np.random.randint(low=1e2, high=1e8)),
save_format='jpg'):
i += 1
if i > 5:
break # Break the cycle after having created 6 augmented images per image, otherwise the generator would loop indefinitely
Read in and preprocess Controlval images¶
In [7]:
# Define the image size
image_size = (512, 512)
# Read in the validation images
Controlval_dir = pathtoimagesControlval
Controlval_files = os.listdir(Controlval_dir)
# For each image
for f in Controlval_files:
# Open the image
img = Image.open(Controlval_dir + f)
# Resize the image so that it has a size 512x512
img = img.resize(image_size)
# Transform into a numpy array with no page number and save it into the preprocessed folder
img_arr = np.array(img)
img_arr[462:512, 0:100, :] = np.mean(img_arr[452:462, 0:100, :])
processed_img = Image.fromarray(img_arr, 'RGB')
processed_img_name = './data/processedControlval/'+'processed'+str(np.random.randint(low=1, high=1e8))+ \
str(np.random.randint(low=1e4, high=1e6))+str(np.random.randint(low=1e4, high=1e6))+ \
str(np.random.randint(low=1e4, high=1e6))+str(np.random.randint(low=1e5, high=1e8))+ \
str(np.random.randint(low=1e2, high=1e7))+str(np.random.randint(low=1e3, high=1e5))+ \
str(np.random.randint(low=1e2, high=1e8))+'.jpg'
processed_img.save(processed_img_name)
Read in, preprocess, and augment CMtrain images¶
In [8]:
# Define the image size
image_size = (512, 512)
# Read in the training images
CMtrain_dir = pathtoimagesCMtrain
CMtrain_files = os.listdir(CMtrain_dir)
# For each image
for f in CMtrain_files:
# Open the image
img = Image.open(CMtrain_dir + f)
# Resize the image so that it has a size 512x512
img = img.resize(image_size)
# Transform into a numpy array with no page number and save it into the preprocessed folder
img_arr = np.array(img)
img_arr[462:512, 0:100, :] = np.mean(img_arr[452:462, 0:100, :])
processed_img = Image.fromarray(img_arr, 'RGB')
processed_img_name = './data/processedCMtrain/'+'processed'+str(np.random.randint(low=1, high=1e8))+ \
str(np.random.randint(low=1e4, high=1e6))+str(np.random.randint(low=1e4, high=1e6))+ \
str(np.random.randint(low=1e4, high=1e6))+str(np.random.randint(low=1e5, high=1e8))+ \
str(np.random.randint(low=1e2, high=1e7))+str(np.random.randint(low=1e3, high=1e5))+ \
str(np.random.randint(low=1e2, high=1e8))+'.jpg'
processed_img.save(processed_img_name)
In [9]:
# Define the characteristics for data augmentation
datagen = ImageDataGenerator(
rotation_range=25,
width_shift_range=0.15,
height_shift_range=0.15,
shear_range=0.15,
zoom_range=0.25,
horizontal_flip=True,
fill_mode='nearest')
# Path to images
ProcessedCMtrain_files = os.listdir(pathtoprocessedimagesCMtrain)
# Augment the images
ProcessedCMtrain_dir = pathtoprocessedimagesCMtrain
for f in ProcessedCMtrain_files:
img = load_img(ProcessedCMtrain_dir + f)
x = img_to_array(img)
x = x.reshape((1,) + x.shape)
# Save the augmented images into a directory of augmented images
i = 0
for batch in datagen.flow(x, batch_size=1,
save_to_dir=pathtoaugmentedimagesCMtrain, save_prefix='augmented'+str(np.random.randint(low=1e5, high=1e8))+str(np.random.randint(low=1e3, high=1e5))+str(np.random.randint(low=1e2, high=1e7))+
str(np.random.randint(low=1e4, high=1e6))+str(np.random.randint(low=1e2, high=1e8))+str(np.random.randint(low=1e4, high=1e6))+str(np.random.randint(low=1e4, high=1e6))
, save_format='jpg'):
i += 1
if i > 5:
break # Break the cycle after having created 6 augmented images per image, otherwise the generator would loop indefinitely
Read in and preprocess CMval images¶
In [10]:
# Define the image size
image_size = (512, 512)
# Read in the validation images
CMval_dir = pathtoimagesCMval
CMval_files = os.listdir(CMval_dir)
# For each image
for f in CMval_files:
# Open the image
img = Image.open(CMval_dir + f)
# Resize the image so that it has a size 512x512
img = img.resize(image_size)
# Transform into a numpy array with no page number and save it into the preprocessed folder
img_arr = np.array(img)
img_arr[462:512, 0:100, :] = np.mean(img_arr[452:462, 0:100, :])
processed_img = Image.fromarray(img_arr, 'RGB')
processed_img_name = './data/processedCMval/'+'processed'+str(np.random.randint(low=1, high=1e8))+ \
str(np.random.randint(low=1e4, high=1e6))+str(np.random.randint(low=1e4, high=1e6))+ \
str(np.random.randint(low=1e4, high=1e6))+str(np.random.randint(low=1e5, high=1e8))+ \
str(np.random.randint(low=1e2, high=1e7))+str(np.random.randint(low=1e3, high=1e5))+ \
str(np.random.randint(low=1e2, high=1e8))+'.jpg'
processed_img.save(processed_img_name)
Read in, preprocess, and augment PVNHtrain images¶
In [11]:
# Define the image size
image_size = (512, 512)
# Read in the training images
PVNHtrain_dir = pathtoimagesPVNHtrain
PVNHtrain_files = os.listdir(PVNHtrain_dir)
# For each image
for f in PVNHtrain_files:
# Open the image
img = Image.open(PVNHtrain_dir + f)
# Resize the image so that it has a size 512x512
img = img.resize(image_size)
# Transform into a numpy array with no page number and save it into the preprocessed folder
img_arr = np.array(img)
img_arr[462:512, 0:100, :] = np.mean(img_arr[452:462, 0:100, :])
processed_img = Image.fromarray(img_arr, 'RGB')
processed_img_name = './data/processedPVNHtrain/'+'processed'+str(np.random.randint(low=1, high=1e8))+ \
str(np.random.randint(low=1e4, high=1e6))+str(np.random.randint(low=1e4, high=1e6))+ \
str(np.random.randint(low=1e4, high=1e6))+str(np.random.randint(low=1e5, high=1e8))+ \
str(np.random.randint(low=1e2, high=1e7))+str(np.random.randint(low=1e3, high=1e5))+ \
str(np.random.randint(low=1e2, high=1e8))+'.jpg'
processed_img.save(processed_img_name)
In [12]:
# Define the characteristics for data augmentation
datagen = ImageDataGenerator(
rotation_range=25,
width_shift_range=0.15,
height_shift_range=0.15,
shear_range=0.15,
zoom_range=0.25,
horizontal_flip=True,
fill_mode='nearest')
# Path to images
ProcessedPVNHtrain_files = os.listdir(pathtoprocessedimagesPVNHtrain)
# Augment the images
ProcessedPVNHtrain_dir = pathtoprocessedimagesPVNHtrain
for f in ProcessedPVNHtrain_files:
img = load_img(ProcessedPVNHtrain_dir + f)
x = img_to_array(img)
x = x.reshape((1,) + x.shape)
# Save the augmented images into a directory of augmented images
i = 0
for batch in datagen.flow(x, batch_size=1,
save_to_dir=pathtoaugmentedimagesPVNHtrain, save_prefix='augmented'+str(np.random.randint(low=1e5, high=1e8))+str(np.random.randint(low=1e3, high=1e5))+str(np.random.randint(low=1e2, high=1e7))+
str(np.random.randint(low=1e4, high=1e6))+str(np.random.randint(low=1e2, high=1e8))+str(np.random.randint(low=1e4, high=1e6))+str(np.random.randint(low=1e4, high=1e6))
, save_format='jpg'):
i += 1
if i > 5:
break # Break the cycle after having created 6 augmented images per image, otherwise the generator would loop indefinitely
Read in and preprocess PVNHval images¶
In [13]:
# Define the image size
image_size = (512, 512)
# Read in the validation images
PVNHval_dir = pathtoimagesPVNHval
PVNHval_files = os.listdir(PVNHval_dir)
# For each image
for f in PVNHval_files:
# Open the image
img = Image.open(PVNHval_dir + f)
# Resize the image so that it has a size 512x512
img = img.resize(image_size)
# Transform into a numpy array with no page number and save it into the preprocessed folder
img_arr = np.array(img)
img_arr[462:512, 0:100, :] = np.mean(img_arr[452:462, 0:100, :])
processed_img = Image.fromarray(img_arr, 'RGB')
processed_img_name = './data/processedPVNHval/'+'processed'+str(np.random.randint(low=1, high=1e8))+ \
str(np.random.randint(low=1e4, high=1e6))+str(np.random.randint(low=1e4, high=1e6))+ \
str(np.random.randint(low=1e4, high=1e6))+str(np.random.randint(low=1e5, high=1e8))+ \
str(np.random.randint(low=1e2, high=1e7))+str(np.random.randint(low=1e3, high=1e5))+ \
str(np.random.randint(low=1e2, high=1e8))+'.jpg'
processed_img.save(processed_img_name)
SECOND PART: IMPORTATION OF FINAL DATA¶
Path to the final images¶
In [14]:
# Create directories for the final images
!mkdir ~/data/FinalControltrain
!mkdir ~/data/FinalControlval
!mkdir ~/data/FinalCMtrain
!mkdir ~/data/FinalCMval
!mkdir ~/data/FinalPVNHtrain
!mkdir ~/data/FinalPVNHval
In [15]:
# Copy all processed images and augmented images to the final folders
!cp ./data/processedControltrain/* ./data/FinalControltrain/
!cp ./data/augmentedControltrain/* ./data/FinalControltrain/
!cp ./data/processedControlval/* ./data/FinalControlval/
!cp ./data/processedCMtrain/* ./data/FinalCMtrain/
!cp ./data/augmentedCMtrain/* ./data/FinalCMtrain/
!cp ./data/processedCMval/* ./data/FinalCMval/
!cp ./data/processedPVNHtrain/* ./data/FinalPVNHtrain/
!cp ./data/augmentedPVNHtrain/* ./data/FinalPVNHtrain/
!cp ./data/processedPVNHval/* ./data/FinalPVNHval/
In [16]:
## Path to final images
pathtofinalControltrain = './data/FinalControltrain/'
pathtofinalControlval = './data/FinalControlval/'
pathtofinalCMtrain = './data/FinalCMtrain/'
pathtofinalCMval = './data/FinalCMval/'
pathtofinalPVNHtrain = './data/FinalPVNHtrain/'
pathtofinalPVNHval = './data/FinalPVNHval/'
Import images and labels for the train set¶
In [17]:
## CONTROLS
# Define the image size
image_size = (512, 512)
# Read in the training images for controls
Controltrain_images = []
Controltrain_dir = pathtofinalControltrain
Controltrain_files = os.listdir(Controltrain_dir)
# For each image
for f in Controltrain_files:
# Open the image
img = Image.open(Controltrain_dir + f)
# Resize the image so that it has a size 512x512
img = img.resize(image_size)
# Transform into a numpy array
img_arr = np.array(img)
# Add the image to the array of images
Controltrain_images.append(img_arr)
# After having transformed all images, transform the list into a numpy array
Controltrain_X = np.array(Controltrain_images)
# Create an array of labels (0 for controls)
Controltrain_y = np.array([[0]*Controltrain_X.shape[0]]).T
## DIFFUSE CM
# Read in the training images for CM
CMtrain_images = []
CMtrain_dir = pathtofinalCMtrain
CMtrain_files = os.listdir(CMtrain_dir)
# For each image
for f in CMtrain_files:
# Open the image
img = Image.open(CMtrain_dir + f)
# Resize the image so that it has a size 512x512
img = img.resize(image_size)
# Transform into a numpy array
img_arr = np.array(img)
# Add the image to the array of images
CMtrain_images.append(img_arr)
# After having transformed all images, transform the list into a numpy array
CMtrain_X = np.array(CMtrain_images)
# Create an array of labels (1 for CM)
CMtrain_y = np.array([[1]*CMtrain_X.shape[0]]).T
## PVNH
# Read in the training images for PVNH
PVNHtrain_images = []
PVNHtrain_dir = pathtofinalPVNHtrain
PVNHtrain_files = os.listdir(PVNHtrain_dir)
# For each image
for f in PVNHtrain_files:
# Open the image
img = Image.open(PVNHtrain_dir + f)
# Resize the image so that it has a size 512x512
img = img.resize(image_size)
# Transform into a numpy array
img_arr = np.array(img)
# Add the image to the array of images
PVNHtrain_images.append(img_arr)
# After having transformed all images, transform the list into a numpy array
PVNHtrain_X = np.array(PVNHtrain_images)
# Create an array of labels (2 for PVNH)
PVNHtrain_y = np.array([[2]*PVNHtrain_X.shape[0]]).T
## MERGE CONTROLS, DIFFUSE CM, AND PVNH
# Train merge files
train_X = np.concatenate([Controltrain_X, CMtrain_X, PVNHtrain_X])
train_y = np.vstack((Controltrain_y, CMtrain_y, PVNHtrain_y))
# GPU expects values to be 32-bit floats
train_X = train_X.astype(np.float32)
# Rescale the pixel values to be between 0 and 1
train_X /= 255.
In [18]:
# Shuffle in unison the train_X and the train_y array (123 is just a random number for reproducibility)
shuffled_train_X, shuffled_train_y = shuffle(train_X, train_y, random_state=123)
# Transform outcome to one-hot encoding
shuffled_train_y = to_categorical(shuffled_train_y)
In [19]:
# Make sure that the dimensions are as expected
shuffled_train_X.shape
Out[19]:
(8108, 512, 512, 3)
In [20]:
# Example of an image to make sure they were converted right
plt.imshow(shuffled_train_X[0])
plt.grid(b=None)
plt.xticks([])
plt.yticks([])
plt.show()
In [21]:
# Make sure that the dimensions are as expected
shuffled_train_y.shape
Out[21]:
(8108, 3)
In [22]:
# Make sure that the label is correct for the image
shuffled_train_y[0]
Out[22]:
array([1., 0., 0.], dtype=float32)
Import images and labels for the validation set¶
In [23]:
## VALIDATION
# Define the image size
image_size = (512, 512)
# Read in the validation images for controls
Controlval_images = []
Controlval_dir = pathtofinalControlval
Controlval_files = os.listdir(Controlval_dir)
# For each image
for f in Controlval_files:
# Open the image
img = Image.open(Controlval_dir + f)
# Resize the image so that it has a size 512x512
img = img.resize(image_size)
# Transform into a numpy array
img_arr = np.array(img)
# Add the image to the array of images
Controlval_images.append(img_arr)
# After having transformed all images, transform the list into a numpy array
Controlval_X = np.array(Controlval_images)
# Create an array of labels (0 for controls)
Controlval_y = np.array([[0]*Controlval_X.shape[0]]).T
## DIFFUSE CM
# Read in the validation images for CM
CMval_images = []
CMval_dir = pathtofinalCMval
CMval_files = os.listdir(CMval_dir)
# For each image
for f in CMval_files:
# Open the image
img = Image.open(CMval_dir + f)
# Resize the image so that it has a size 512x512
img = img.resize(image_size)
# Transform into a numpy array
img_arr = np.array(img)
# Add the image to the array of images
CMval_images.append(img_arr)
# After having transformed all images, transform the list into a numpy array
CMval_X = np.array(CMval_images)
# Create an array of labels (1 for CM)
CMval_y = np.array([[1]*CMval_X.shape[0]]).T
## PVNH
# Read in the validation images for PVNH
PVNHval_images = []
PVNHval_dir = pathtofinalPVNHval
PVNHval_files = os.listdir(PVNHval_dir)
# For each image
for f in PVNHval_files:
# Open the image
img = Image.open(PVNHval_dir + f)
# Resize the image so that it has a size 512x512
img = img.resize(image_size)
# Transform into a numpy array
img_arr = np.array(img)
# Add the image to the array of images
PVNHval_images.append(img_arr)
# After having transformed all images, transform the list into a numpy array
PVNHval_X = np.array(PVNHval_images)
# Create an array of labels (2 for PVNH)
PVNHval_y = np.array([[2]*PVNHval_X.shape[0]]).T
## MERGE CONTROLS, DIFFUSE CM, AND PVNH
# Val merge files
val_X = np.concatenate([Controlval_X, CMval_X, PVNHval_X])
val_y = np.vstack((Controlval_y, CMval_y, PVNHval_y))
# GPU expects pixel values to be 32-bit floats
val_X = val_X.astype(np.float32)
# Rescale the pixel values to be between 0 and 1
val_X /= 255.
In [24]:
# Shuffle in unison the val_X and the val_y array (123 is just a random number for reproducibility)
shuffled_val_X, shuffled_val_y = shuffle(val_X, val_y, random_state=123)
# Transform outcome to one-hot encoding
shuffled_val_y = to_categorical(shuffled_val_y)
In [25]:
# Make sure that the dimensions are as expected
shuffled_val_X.shape
Out[25]:
(481, 512, 512, 3)
In [26]:
# Example of an image to make sure they were converted right
plt.imshow(shuffled_val_X[0])
plt.grid(b=None)
plt.xticks([])
plt.yticks([])
plt.show()
In [27]:
# Make sure that the dimensions are as expected
shuffled_val_y.shape
Out[27]:
(481, 3)
In [28]:
# Make sure that the label is correct for the image
shuffled_val_y[0]
Out[28]:
array([0., 0., 1.], dtype=float32)
THIRD PART: EVALUATE NEURAL NETWORKS¶
CNNMCD (CNN architecture created by the authors)¶
In [29]:
## Define the initial input
initial_input = Input(shape = train_X.shape[1:])
## Add convolutional and max pooling layers
x = Conv2D(filters = 64, kernel_size = (3,3), padding = 'same')(initial_input)
x = MaxPooling2D(pool_size = (2, 2), padding = 'same')(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = Conv2D(filters = 64, kernel_size = (3,3), padding = 'same', activation='relu')(x)
x = MaxPooling2D(pool_size = (2, 2), padding = 'same')(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
## Add inception block
# Convolution 1x1
conv1 = Conv2D(filters=64, kernel_size=(1,1), padding='same', activation='relu')(x)
# Convolution 3x3
conv3 = Conv2D(filters=96, kernel_size=(1,1), padding='same', activation='relu')(x)
conv3 = Conv2D(filters=128, kernel_size=(3,3), padding='same', activation='relu')(conv3)
# Convolution 5x5
conv5 = Conv2D(filters=64, kernel_size=(1,1), padding='same', activation='relu')(x)
conv5 = Conv2D(filters=128, kernel_size=(5,5), padding='same', activation='relu')(conv5)
# Max Pooling 3x3
pool = MaxPooling2D(pool_size=(3,3), strides=(1,1), padding='same')(x)
pool = Conv2D(filters=64, kernel_size=(3,3), padding='same', activation='relu')(pool)
# Concatenate filters
x = concatenate([conv1, conv3, conv5, pool], axis=-1)
## Add convolutional and max pooling layers
x = Conv2D(filters = 128, kernel_size = (3,3), padding = 'same')(initial_input)
x = MaxPooling2D(pool_size = (2, 2), padding = 'same')(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = Conv2D(filters = 256, kernel_size = (3,3), padding = 'same', activation='relu')(x)
x = MaxPooling2D(pool_size = (2, 2), padding = 'same')(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = Conv2D(filters = 256, kernel_size = (3,3), padding = 'same', activation='relu')(x)
x = MaxPooling2D(pool_size = (2, 2), padding = 'same')(x)
## Add inception block
# Convolution 1x1
conv1 = Conv2D(filters=128, kernel_size=(1,1), padding='same', activation='relu')(x)
# Convolution 3x3
conv3 = Conv2D(filters=128, kernel_size=(1,1), padding='same', activation='relu')(x)
conv3 = Conv2D(filters=256, kernel_size=(3,3), padding='same', activation='relu')(conv3)
# Convolution 5x5
conv5 = Conv2D(filters=64, kernel_size=(1,1), padding='same', activation='relu')(x)
conv5 = Conv2D(filters=128, kernel_size=(5,5), padding='same', activation='relu')(conv5)
# Max Pooling 3x3
pool = MaxPooling2D(pool_size=(3,3), strides=(1,1), padding='same')(x)
pool = Conv2D(filters=64, kernel_size=(3,3), padding='same', activation='relu')(pool)
# Concatenate filters
x = concatenate([conv1, conv3, conv5, pool], axis=-1)
## Add inception block
# Convolution 1x1
conv1 = Conv2D(filters=128, kernel_size=(1,1), padding='same', activation='relu')(x)
# Convolution 3x3
conv3 = Conv2D(filters=128, kernel_size=(1,1), padding='same', activation='relu')(x)
conv3 = Conv2D(filters=256, kernel_size=(3,3), padding='same', activation='relu')(conv3)
# Convolution 5x5
conv5 = Conv2D(filters=64, kernel_size=(1,1), padding='same', activation='relu')(x)
conv5 = Conv2D(filters=128, kernel_size=(5,5), padding='same', activation='relu')(conv5)
# Max Pooling 3x3
pool = MaxPooling2D(pool_size=(3,3), strides=(1,1), padding='same')(x)
pool = Conv2D(filters=64, kernel_size=(3,3), padding='same', activation='relu')(pool)
# Concatenate filters
x = concatenate([conv1, conv3, conv5, pool], axis=-1)
## Add inception block
# Convolution 1x1
conv1 = Conv2D(filters=128, kernel_size=(1,1), padding='same', activation='relu')(x)
# Convolution 3x3
conv3 = Conv2D(filters=128, kernel_size=(1,1), padding='same', activation='relu')(x)
conv3 = Conv2D(filters=256, kernel_size=(3,3), padding='same', activation='relu')(conv3)
# Convolution 5x5
conv5 = Conv2D(filters=64, kernel_size=(1,1), padding='same', activation='relu')(x)
conv5 = Conv2D(filters=128, kernel_size=(5,5), padding='same', activation='relu')(conv5)
# Max Pooling 3x3
pool = MaxPooling2D(pool_size=(3,3), strides=(1,1), padding='same')(x)
pool = Conv2D(filters=64, kernel_size=(3,3), padding='same', activation='relu')(pool)
# Concatenate filters
x = concatenate([conv1, conv3, conv5, pool], axis=-1)
## Add inception block
# Convolution 1x1
conv1 = Conv2D(filters=128, kernel_size=(1,1), padding='same', activation='relu')(x)
# Convolution 3x3
conv3 = Conv2D(filters=128, kernel_size=(1,1), padding='same', activation='relu')(x)
conv3 = Conv2D(filters=256, kernel_size=(3,3), padding='same', activation='relu')(conv3)
# Convolution 5x5
conv5 = Conv2D(filters=64, kernel_size=(1,1), padding='same', activation='relu')(x)
conv5 = Conv2D(filters=128, kernel_size=(5,5), padding='same', activation='relu')(conv5)
# Max Pooling 3x3
pool = MaxPooling2D(pool_size=(3,3), strides=(1,1), padding='same')(x)
pool = Conv2D(filters=64, kernel_size=(3,3), padding='same', activation='relu')(pool)
# Concatenate filters
x = concatenate([conv1, conv3, conv5, pool], axis=-1)
## Add global average pooling
x = GlobalAveragePooling2D()(x)
## Add the fully-connected layers
x = Dense(units=516, activation='relu')(x)
x = Dropout(rate=0.5)(x)
x = Dense(units=256, activation='relu')(x)
x = Dropout(rate=0.5)(x)
x = Dense(units=64, activation='relu')(x)
predictions = Dense(units=3, activation='softmax')(x)
# Define the model to be trained
model = Model(inputs=initial_input, outputs=predictions)
# Define the neural network optimizer
opt = Adam(lr = 0.0001)
# Compile the model
model.compile(optimizer = opt, loss = 'categorical_crossentropy', metrics = ['accuracy'])
# Fit the model in the training set
historyCNNMCD = model.fit(shuffled_train_X, shuffled_train_y, validation_data = [shuffled_val_X, shuffled_val_y], epochs = 50, batch_size = 32)
print('\n')
print('\n')
# AUC in train and validation set
auc_trainCNNMCD = roc_auc_score(shuffled_train_y, model.predict(shuffled_train_X))
print('The AUC in the train set is {:.4f}.'.format(auc_trainCNNMCD))
print('\n')
print('\n')
auc_validCNNMCD = roc_auc_score(shuffled_val_y, model.predict(shuffled_val_X))
print('The AUC in the validation set is {:.4f}.'.format(auc_validCNNMCD))
print('\n')
print('\n')
print('\n')
print('\n')
# Figure size and colors
mpl.rcParams['figure.figsize'] = (20,24)
plt.rcParams['axes.facecolor']='white'
plt.rcParams['figure.facecolor']='lightgreen'
plt.rcParams['figure.edgecolor']='black'
# Plot history of loss during training
plt.plot(historyCNNMCD.history['loss'], label='Train', color='red')
plt.plot(historyCNNMCD.history['val_loss'], label='Validation', color='blue')
plt.title('Loss in train and validation set', size=30)
plt.xlabel('Epoch', size=24)
plt.ylabel('Categorical cross-entropy loss', size=24)
plt.xticks(fontsize=20)
plt.yticks(fontsize=20)
plt.legend(fontsize=20)
plt.ylim(ymin=0, ymax=5)
plt.grid(b=None)
plt.show()
print('\n')
print('\n')
print('\n')
print('\n')
# Plot history of accuracy
plt.plot(historyCNNMCD.history['accuracy'], label='Train', color='red')
plt.plot(historyCNNMCD.history['val_accuracy'], label='Validation', color='blue')
plt.title('Accuracy in train and validation set', size=30)
plt.xlabel('Epoch', size=24)
plt.ylabel('Accuracy', size=24)
plt.xticks(fontsize=20)
plt.yticks(fontsize=20)
plt.legend(fontsize=20)
plt.ylim(ymin=0, ymax=1.1)
plt.grid(b=None)
plt.show()
Train on 8108 samples, validate on 481 samples Epoch 1/50 8108/8108 [==============================] - 180s 22ms/sample - loss: 1.0948 - accuracy: 0.3648 - val_loss: 1.0976 - val_accuracy: 0.3306 Epoch 2/50 8108/8108 [==============================] - 169s 21ms/sample - loss: 1.0232 - accuracy: 0.4587 - val_loss: 1.1374 - val_accuracy: 0.3306 Epoch 3/50 8108/8108 [==============================] - 169s 21ms/sample - loss: 0.8216 - accuracy: 0.6217 - val_loss: 1.0984 - val_accuracy: 0.4823 Epoch 4/50 8108/8108 [==============================] - 165s 20ms/sample - loss: 0.6683 - accuracy: 0.7121 - val_loss: 1.4380 - val_accuracy: 0.4615 Epoch 5/50 8108/8108 [==============================] - 166s 20ms/sample - loss: 0.5467 - accuracy: 0.7760 - val_loss: 2.3152 - val_accuracy: 0.4262 Epoch 6/50 8108/8108 [==============================] - 166s 21ms/sample - loss: 0.4248 - accuracy: 0.8330 - val_loss: 1.8493 - val_accuracy: 0.4075 Epoch 7/50 8108/8108 [==============================] - 167s 21ms/sample - loss: 0.3231 - accuracy: 0.8815 - val_loss: 2.6807 - val_accuracy: 0.4116 Epoch 8/50 8108/8108 [==============================] - 166s 21ms/sample - loss: 0.2641 - accuracy: 0.9073 - val_loss: 2.7549 - val_accuracy: 0.4158 Epoch 9/50 8108/8108 [==============================] - 158s 20ms/sample - loss: 0.2038 - accuracy: 0.9261 - val_loss: 3.4472 - val_accuracy: 0.4740 Epoch 10/50 8108/8108 [==============================] - 163s 20ms/sample - loss: 0.1856 - accuracy: 0.9362 - val_loss: 2.4446 - val_accuracy: 0.5114 Epoch 11/50 8108/8108 [==============================] - 160s 20ms/sample - loss: 0.1467 - accuracy: 0.9493 - val_loss: 3.4872 - val_accuracy: 0.4615 Epoch 12/50 8108/8108 [==============================] - 157s 19ms/sample - loss: 0.1269 - accuracy: 0.9581 - val_loss: 2.7704 - val_accuracy: 0.5198 Epoch 13/50 8108/8108 [==============================] - 161s 20ms/sample - loss: 0.0956 - accuracy: 0.9679 - val_loss: 3.3667 - val_accuracy: 0.4407 Epoch 14/50 8108/8108 [==============================] - 161s 20ms/sample - loss: 0.0766 - accuracy: 0.9729 - val_loss: 4.2732 - val_accuracy: 0.3721 Epoch 15/50 8108/8108 [==============================] - 158s 19ms/sample - loss: 0.0783 - accuracy: 0.9748 - val_loss: 5.3528 - val_accuracy: 0.5073 Epoch 16/50 8108/8108 [==============================] - 157s 19ms/sample - loss: 0.0628 - accuracy: 0.9789 - val_loss: 3.4042 - val_accuracy: 0.4574 Epoch 17/50 8108/8108 [==============================] - 156s 19ms/sample - loss: 0.0702 - accuracy: 0.9777 - val_loss: 5.0784 - val_accuracy: 0.4636 Epoch 18/50 8108/8108 [==============================] - 156s 19ms/sample - loss: 0.0520 - accuracy: 0.9825 - val_loss: 4.3980 - val_accuracy: 0.5073 Epoch 19/50 8108/8108 [==============================] - 156s 19ms/sample - loss: 0.0442 - accuracy: 0.9864 - val_loss: 4.6937 - val_accuracy: 0.4657 Epoch 20/50 8108/8108 [==============================] - 156s 19ms/sample - loss: 0.0387 - accuracy: 0.9868 - val_loss: 3.9826 - val_accuracy: 0.5114 Epoch 21/50 8108/8108 [==============================] - 155s 19ms/sample - loss: 0.0450 - accuracy: 0.9858 - val_loss: 4.7025 - val_accuracy: 0.4387 Epoch 22/50 8108/8108 [==============================] - 156s 19ms/sample - loss: 0.0553 - accuracy: 0.9826 - val_loss: 5.7066 - val_accuracy: 0.5177 Epoch 23/50 8108/8108 [==============================] - 155s 19ms/sample - loss: 0.0460 - accuracy: 0.9864 - val_loss: 3.6804 - val_accuracy: 0.4948 Epoch 24/50 8108/8108 [==============================] - 156s 19ms/sample - loss: 0.0315 - accuracy: 0.9906 - val_loss: 4.2388 - val_accuracy: 0.5052 Epoch 25/50 8108/8108 [==============================] - 155s 19ms/sample - loss: 0.0318 - accuracy: 0.9901 - val_loss: 4.4303 - val_accuracy: 0.4844 Epoch 26/50 8108/8108 [==============================] - 155s 19ms/sample - loss: 0.0314 - accuracy: 0.9896 - val_loss: 4.5770 - val_accuracy: 0.4511 Epoch 27/50 8108/8108 [==============================] - 157s 19ms/sample - loss: 0.0309 - accuracy: 0.9909 - val_loss: 4.0583 - val_accuracy: 0.4699 Epoch 28/50 8108/8108 [==============================] - 157s 19ms/sample - loss: 0.0358 - accuracy: 0.9877 - val_loss: 4.8014 - val_accuracy: 0.4262 Epoch 29/50 8108/8108 [==============================] - 156s 19ms/sample - loss: 0.0302 - accuracy: 0.9910 - val_loss: 4.6751 - val_accuracy: 0.4324 Epoch 30/50 8108/8108 [==============================] - 155s 19ms/sample - loss: 0.0307 - accuracy: 0.9901 - val_loss: 5.2663 - val_accuracy: 0.3929 Epoch 31/50 8108/8108 [==============================] - 152s 19ms/sample - loss: 0.0255 - accuracy: 0.9921 - val_loss: 5.9204 - val_accuracy: 0.4449 Epoch 32/50 8108/8108 [==============================] - 155s 19ms/sample - loss: 0.0247 - accuracy: 0.9922 - val_loss: 5.2696 - val_accuracy: 0.4844 Epoch 33/50 8108/8108 [==============================] - 156s 19ms/sample - loss: 0.0103 - accuracy: 0.9963 - val_loss: 5.1959 - val_accuracy: 0.4782 Epoch 34/50 8108/8108 [==============================] - 155s 19ms/sample - loss: 0.0406 - accuracy: 0.9875 - val_loss: 3.8797 - val_accuracy: 0.5094 Epoch 35/50 8108/8108 [==============================] - 153s 19ms/sample - loss: 0.0248 - accuracy: 0.9922 - val_loss: 3.9202 - val_accuracy: 0.5135 Epoch 36/50 8108/8108 [==============================] - 155s 19ms/sample - loss: 0.0187 - accuracy: 0.9941 - val_loss: 5.4186 - val_accuracy: 0.4366 Epoch 37/50 8108/8108 [==============================] - 156s 19ms/sample - loss: 0.0290 - accuracy: 0.9906 - val_loss: 5.4818 - val_accuracy: 0.4511 Epoch 38/50 8108/8108 [==============================] - 155s 19ms/sample - loss: 0.0147 - accuracy: 0.9952 - val_loss: 3.5797 - val_accuracy: 0.5094 Epoch 39/50 8108/8108 [==============================] - 153s 19ms/sample - loss: 0.0266 - accuracy: 0.9919 - val_loss: 5.3169 - val_accuracy: 0.4179 Epoch 40/50 8108/8108 [==============================] - 155s 19ms/sample - loss: 0.0229 - accuracy: 0.9937 - val_loss: 3.7509 - val_accuracy: 0.4865 Epoch 41/50 8108/8108 [==============================] - 155s 19ms/sample - loss: 0.0159 - accuracy: 0.9958 - val_loss: 5.7549 - val_accuracy: 0.4969 Epoch 42/50 8108/8108 [==============================] - 152s 19ms/sample - loss: 0.0289 - accuracy: 0.9917 - val_loss: 4.6865 - val_accuracy: 0.4699 Epoch 43/50 8108/8108 [==============================] - 155s 19ms/sample - loss: 0.0113 - accuracy: 0.9964 - val_loss: 5.9384 - val_accuracy: 0.4491 Epoch 44/50 8108/8108 [==============================] - 155s 19ms/sample - loss: 0.0160 - accuracy: 0.9947 - val_loss: 5.6950 - val_accuracy: 0.5198 Epoch 45/50 8108/8108 [==============================] - 153s 19ms/sample - loss: 0.0270 - accuracy: 0.9917 - val_loss: 5.6194 - val_accuracy: 0.4179 Epoch 46/50 8108/8108 [==============================] - 156s 19ms/sample - loss: 0.0185 - accuracy: 0.9946 - val_loss: 4.1424 - val_accuracy: 0.5364 Epoch 47/50 8108/8108 [==============================] - 155s 19ms/sample - loss: 0.0164 - accuracy: 0.9949 - val_loss: 5.1806 - val_accuracy: 0.4511 Epoch 48/50 8108/8108 [==============================] - 152s 19ms/sample - loss: 0.0013 - accuracy: 0.9996 - val_loss: 7.2886 - val_accuracy: 0.4283 Epoch 49/50 8108/8108 [==============================] - 155s 19ms/sample - loss: 0.0186 - accuracy: 0.9954 - val_loss: 4.1215 - val_accuracy: 0.5613 Epoch 50/50 8108/8108 [==============================] - 154s 19ms/sample - loss: 0.0358 - accuracy: 0.9884 - val_loss: 5.9929 - val_accuracy: 0.4719 The AUC in the train set is 1.0000. The AUC in the validation set is 0.7034.
In [30]:
# Generate predictions in the form of probabilities for the validation set
valCNNMCD = model.predict(shuffled_val_X, batch_size = 32)
# Generate the confusion matrix in the validation set
y_true = np.argmax(shuffled_val_y, axis=1)
y_predCNNMCD = np.argmax(valCNNMCD, axis=1)
# Confusion matrix
pd.DataFrame(confusion_matrix(y_true, y_predCNNMCD), index=['True: Normal', 'True: Diffuse CM', 'True: PVNH'], columns=['Prediction: Normal', 'Prediction: Diffuse CM', 'Prediction: PVNH']).T
Out[30]:
| True: Normal | True: Diffuse CM | True: PVNH | |
|---|---|---|---|
| Prediction: Normal | 88 | 16 | 110 |
| Prediction: Diffuse CM | 33 | 109 | 35 |
| Prediction: PVNH | 38 | 22 | 30 |
In [31]:
# Calculate accuracy in the validation set
accuracy_CNNMCD = accuracy_score(y_true=y_true, y_pred=y_predCNNMCD)
print('The accuracy in the validation set is {:.4f}.'.format(accuracy_CNNMCD))
The accuracy in the validation set is 0.4719.
In [32]:
# Calculate AUC in the validation set
auc_CNNMCD = roc_auc_score(shuffled_val_y, model.predict(shuffled_val_X))
print('The AUC in the validation set is {:.4f}.'.format(auc_validCNNMCD))
The AUC in the validation set is 0.7034.
In [33]:
# Classification report
print(classification_report(y_true, y_predCNNMCD, target_names=['Normal MRI', 'Diffuse CM', 'PVNH']))
precision recall f1-score support
Normal MRI 0.41 0.55 0.47 159
Diffuse CM 0.62 0.74 0.67 147
PVNH 0.33 0.17 0.23 175
accuracy 0.47 481
macro avg 0.45 0.49 0.46 481
weighted avg 0.45 0.47 0.44 481
Save model CNNMCD¶
In [34]:
# Serialize model to JSON
model_json = model.to_json()
with open("CNNMCD.json", "w") as json_file:
json_file.write(model_json)
# Serialize weights to HDF5
model.save_weights("CNNMCD.h5")
Model visualization¶
In [35]:
# Visualize the structure and layers of the model
model.layers
Out[35]:
[<tensorflow.python.keras.engine.input_layer.InputLayer at 0x7f3388ef9f60>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f335b03acf8>, <tensorflow.python.keras.layers.pooling.MaxPooling2D at 0x7f335b03af28>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f335b040c18>, <tensorflow.python.keras.layers.core.Activation at 0x7f335b040cc0>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f335b0540f0>, <tensorflow.python.keras.layers.pooling.MaxPooling2D at 0x7f335b04aeb8>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f335affbd68>, <tensorflow.python.keras.layers.core.Activation at 0x7f335affb9e8>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f335b002c50>, <tensorflow.python.keras.layers.pooling.MaxPooling2D at 0x7f335b002cf8>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f335afaf6d8>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f335afb8dd8>, <tensorflow.python.keras.layers.pooling.MaxPooling2D at 0x7f335afd2eb8>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f335afafa58>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f335afb8b00>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f335afc2ac8>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f335afcaf28>, <tensorflow.python.keras.layers.merge.Concatenate at 0x7f335afd2c50>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f335afdcc50>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f335afeef28>, <tensorflow.python.keras.layers.pooling.MaxPooling2D at 0x7f335af828d0>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f335afdcfd0>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f335afe5dd8>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f335afeeef0>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f335af77e80>, <tensorflow.python.keras.layers.merge.Concatenate at 0x7f335af8a3c8>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f335af92b00>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f335af9bd30>, <tensorflow.python.keras.layers.pooling.MaxPooling2D at 0x7f335af35d68>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f335af92748>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f335af9b9e8>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f335afa3978>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f335afaedd8>, <tensorflow.python.keras.layers.merge.Concatenate at 0x7f335af35b00>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f335af40940>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f335af51dd8>, <tensorflow.python.keras.layers.pooling.MaxPooling2D at 0x7f335af64780>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f335af40e80>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f335af48c88>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f335af51da0>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f335af5cda0>, <tensorflow.python.keras.layers.merge.Concatenate at 0x7f335af6c278>, <tensorflow.python.keras.layers.pooling.GlobalAveragePooling2D at 0x7f335aef55f8>, <tensorflow.python.keras.layers.core.Dense at 0x7f335aef59b0>, <tensorflow.python.keras.layers.core.Dropout at 0x7f335aef54a8>, <tensorflow.python.keras.layers.core.Dense at 0x7f335aeffe10>, <tensorflow.python.keras.layers.core.Dropout at 0x7f335aeff898>, <tensorflow.python.keras.layers.core.Dense at 0x7f335af06898>, <tensorflow.python.keras.layers.core.Dense at 0x7f335af19940>]
In [36]:
# Visualize the structure and layers of the model
print(model.summary())
Model: "model"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_1 (InputLayer) [(None, 512, 512, 3) 0
__________________________________________________________________________________________________
conv2d_8 (Conv2D) (None, 512, 512, 128 3584 input_1[0][0]
__________________________________________________________________________________________________
max_pooling2d_3 (MaxPooling2D) (None, 256, 256, 128 0 conv2d_8[0][0]
__________________________________________________________________________________________________
batch_normalization_2 (BatchNor (None, 256, 256, 128 512 max_pooling2d_3[0][0]
__________________________________________________________________________________________________
activation_2 (Activation) (None, 256, 256, 128 0 batch_normalization_2[0][0]
__________________________________________________________________________________________________
conv2d_9 (Conv2D) (None, 256, 256, 256 295168 activation_2[0][0]
__________________________________________________________________________________________________
max_pooling2d_4 (MaxPooling2D) (None, 128, 128, 256 0 conv2d_9[0][0]
__________________________________________________________________________________________________
batch_normalization_3 (BatchNor (None, 128, 128, 256 1024 max_pooling2d_4[0][0]
__________________________________________________________________________________________________
activation_3 (Activation) (None, 128, 128, 256 0 batch_normalization_3[0][0]
__________________________________________________________________________________________________
conv2d_10 (Conv2D) (None, 128, 128, 256 590080 activation_3[0][0]
__________________________________________________________________________________________________
max_pooling2d_5 (MaxPooling2D) (None, 64, 64, 256) 0 conv2d_10[0][0]
__________________________________________________________________________________________________
conv2d_12 (Conv2D) (None, 64, 64, 128) 32896 max_pooling2d_5[0][0]
__________________________________________________________________________________________________
conv2d_14 (Conv2D) (None, 64, 64, 64) 16448 max_pooling2d_5[0][0]
__________________________________________________________________________________________________
max_pooling2d_6 (MaxPooling2D) (None, 64, 64, 256) 0 max_pooling2d_5[0][0]
__________________________________________________________________________________________________
conv2d_11 (Conv2D) (None, 64, 64, 128) 32896 max_pooling2d_5[0][0]
__________________________________________________________________________________________________
conv2d_13 (Conv2D) (None, 64, 64, 256) 295168 conv2d_12[0][0]
__________________________________________________________________________________________________
conv2d_15 (Conv2D) (None, 64, 64, 128) 204928 conv2d_14[0][0]
__________________________________________________________________________________________________
conv2d_16 (Conv2D) (None, 64, 64, 64) 147520 max_pooling2d_6[0][0]
__________________________________________________________________________________________________
concatenate_1 (Concatenate) (None, 64, 64, 576) 0 conv2d_11[0][0]
conv2d_13[0][0]
conv2d_15[0][0]
conv2d_16[0][0]
__________________________________________________________________________________________________
conv2d_18 (Conv2D) (None, 64, 64, 128) 73856 concatenate_1[0][0]
__________________________________________________________________________________________________
conv2d_20 (Conv2D) (None, 64, 64, 64) 36928 concatenate_1[0][0]
__________________________________________________________________________________________________
max_pooling2d_7 (MaxPooling2D) (None, 64, 64, 576) 0 concatenate_1[0][0]
__________________________________________________________________________________________________
conv2d_17 (Conv2D) (None, 64, 64, 128) 73856 concatenate_1[0][0]
__________________________________________________________________________________________________
conv2d_19 (Conv2D) (None, 64, 64, 256) 295168 conv2d_18[0][0]
__________________________________________________________________________________________________
conv2d_21 (Conv2D) (None, 64, 64, 128) 204928 conv2d_20[0][0]
__________________________________________________________________________________________________
conv2d_22 (Conv2D) (None, 64, 64, 64) 331840 max_pooling2d_7[0][0]
__________________________________________________________________________________________________
concatenate_2 (Concatenate) (None, 64, 64, 576) 0 conv2d_17[0][0]
conv2d_19[0][0]
conv2d_21[0][0]
conv2d_22[0][0]
__________________________________________________________________________________________________
conv2d_24 (Conv2D) (None, 64, 64, 128) 73856 concatenate_2[0][0]
__________________________________________________________________________________________________
conv2d_26 (Conv2D) (None, 64, 64, 64) 36928 concatenate_2[0][0]
__________________________________________________________________________________________________
max_pooling2d_8 (MaxPooling2D) (None, 64, 64, 576) 0 concatenate_2[0][0]
__________________________________________________________________________________________________
conv2d_23 (Conv2D) (None, 64, 64, 128) 73856 concatenate_2[0][0]
__________________________________________________________________________________________________
conv2d_25 (Conv2D) (None, 64, 64, 256) 295168 conv2d_24[0][0]
__________________________________________________________________________________________________
conv2d_27 (Conv2D) (None, 64, 64, 128) 204928 conv2d_26[0][0]
__________________________________________________________________________________________________
conv2d_28 (Conv2D) (None, 64, 64, 64) 331840 max_pooling2d_8[0][0]
__________________________________________________________________________________________________
concatenate_3 (Concatenate) (None, 64, 64, 576) 0 conv2d_23[0][0]
conv2d_25[0][0]
conv2d_27[0][0]
conv2d_28[0][0]
__________________________________________________________________________________________________
conv2d_30 (Conv2D) (None, 64, 64, 128) 73856 concatenate_3[0][0]
__________________________________________________________________________________________________
conv2d_32 (Conv2D) (None, 64, 64, 64) 36928 concatenate_3[0][0]
__________________________________________________________________________________________________
max_pooling2d_9 (MaxPooling2D) (None, 64, 64, 576) 0 concatenate_3[0][0]
__________________________________________________________________________________________________
conv2d_29 (Conv2D) (None, 64, 64, 128) 73856 concatenate_3[0][0]
__________________________________________________________________________________________________
conv2d_31 (Conv2D) (None, 64, 64, 256) 295168 conv2d_30[0][0]
__________________________________________________________________________________________________
conv2d_33 (Conv2D) (None, 64, 64, 128) 204928 conv2d_32[0][0]
__________________________________________________________________________________________________
conv2d_34 (Conv2D) (None, 64, 64, 64) 331840 max_pooling2d_9[0][0]
__________________________________________________________________________________________________
concatenate_4 (Concatenate) (None, 64, 64, 576) 0 conv2d_29[0][0]
conv2d_31[0][0]
conv2d_33[0][0]
conv2d_34[0][0]
__________________________________________________________________________________________________
global_average_pooling2d (Globa (None, 576) 0 concatenate_4[0][0]
__________________________________________________________________________________________________
dense (Dense) (None, 516) 297732 global_average_pooling2d[0][0]
__________________________________________________________________________________________________
dropout (Dropout) (None, 516) 0 dense[0][0]
__________________________________________________________________________________________________
dense_1 (Dense) (None, 256) 132352 dropout[0][0]
__________________________________________________________________________________________________
dropout_1 (Dropout) (None, 256) 0 dense_1[0][0]
__________________________________________________________________________________________________
dense_2 (Dense) (None, 64) 16448 dropout_1[0][0]
__________________________________________________________________________________________________
dense_3 (Dense) (None, 3) 195 dense_2[0][0]
==================================================================================================
Total params: 5,116,679
Trainable params: 5,115,911
Non-trainable params: 768
__________________________________________________________________________________________________
None
In [37]:
# Define modifier to replace the sigmoid function of the last layer to a linear function
def model_modifier(m):
m.layers[-1].activation = tf.keras.activations.linear
# Define losses functions. 0 is the index for a normal MRI
loss_normal = lambda output: K.mean(output[:, 0])
# Define losses functions. 1 is the index for a diffuse malformation of cortical development MRI
loss_diffuseMCD = lambda output: K.mean(output[:, 1])
# Define losses functions. 2 is the index for a PVNH MRI
loss_PVNH = lambda output: K.mean(output[:, 2])
# Create Gradcam object
gradcam = Gradcam(model, model_modifier)
# Create Saliency object
saliency = Saliency(model, model_modifier)
# Iterate through the MRIs in test set
# Set background to white color
plt.rcParams['axes.facecolor']='white'
plt.rcParams['figure.facecolor']='white'
plt.rcParams['figure.edgecolor']='white'
print('\n \n' + '\033[1m' + 'EACH ORIGINAL MRI IS ANALYZED WITH TWO METHODS: CLASS ACTIVATION MAP (UPPER ROW) AND SALIENCY MAP (LOWER ROW)' + '\033[0m' + '\n')
print('\033[1m' + 'EACH MAP IS SUPERIMPOSED ON THE ORIGINAL MRI WITH A TRANSPARENCY THAT IS INVERSELY PROPORTIONAL TO THE ESTIMATED PROBABILITY OF THE MRI BELONGING TO THAT CATEGORY (NORMAL MRI, DIFFUSE CORTICAL MALFORMATION, OR PERIVENTRICULAR NODULAR HETEROTOPIA) \n \nHIGHER ESTIMATED PROBABILITIES PRODUCE CLEARLY SEEN MAPS OVERLAID ON THE ORIGINAL MRI AND LOWER ESTIMATED PROBABILITIES PRODUCE VERY TRANSPARENT OR NOT APPRECIABLE MAPS OVERLAID ON THE ORIGINAL MRI' + '\033[0m'+ '\n')
for i in range(20):
# Print spaces to separate from the next image
print('\n \n \n \n \n \n')
# Print real classification of the image
if y_true[i]==0:
real_classification='Normal MRI'
elif y_true[i]==1:
real_classification='Diffuse MCD'
else:
real_classification='PVNH'
print('\033[1m' + 'REAL CLASSIFICATION OF THE IMAGE: {}'.format(real_classification) + '\033[0m')
# Print model classification and model probability of MCD
if y_predCNNMCD[i]==0:
predicted_classification='Normal MRI'
elif y_predCNNMCD[i]==1:
predicted_classification='Diffuse MCD'
else:
predicted_classification='PVNH'
print('\033[1m' + 'MODEL CLASSIFICATION OF THE IMAGE: {}'.format(predicted_classification) + '\033[0m \n')
print('\033[1m' + ' Prob. Normal MRI: {:.4f} '.format(valCNNMCD[i][0]) + 'Prob. Diffuse MCD: {:.4f} '.format(valCNNMCD[i][1]), 'Prob. PVNH: {:.4f}'.format(valCNNMCD[i][2]) + '\033[0m')
# Arrays to plot
original_image=shuffled_val_X[i]
list_heatmaps=[
# GradCam heatmap for normal MRI
normalize(gradcam(loss_normal, shuffled_val_X[i])),
# GradCam heatmap for diffuse MCD
normalize(gradcam(loss_diffuseMCD, shuffled_val_X[i])),
# GradCam heatmap for PVNH
normalize(gradcam(loss_PVNH, shuffled_val_X[i])),
# Saliency heatmap for normal MRI
normalize(saliency(loss_normal, seed_input=np.expand_dims(shuffled_val_X[i], axis=0), smooth_noise=0.2)),
# Saliency heatmap for diffuse MCD
normalize(saliency(loss_diffuseMCD, seed_input=np.expand_dims(shuffled_val_X[i], axis=0), smooth_noise=0.2)),
# Saliency heatmap for PVNH
normalize(saliency(loss_PVNH, seed_input=np.expand_dims(shuffled_val_X[i], axis=0), smooth_noise=0.2))
]
# Define figure
f=plt.figure(figsize=(20, 8))
# Define the image grid
grid = ImageGrid(f, 111,
nrows_ncols=(2, 3),
axes_pad=0.05,
share_all=True,
cbar_location="right",
cbar_mode=None,
cbar_size="2%",
cbar_pad=0.15)
# Iterate over the graphs
for j, axis in enumerate(grid):
# Plot original
im=axis.imshow(original_image)
im=axis.imshow(list_heatmaps[j][0], cmap='jet', alpha=0.5*valCNNMCD[i][j%3])
im=axis.set_xticks([])
im=axis.set_yticks([])
# Create scalarmappable for obtaining the colorbar from 0 to 1
sm = plt.cm.ScalarMappable(cmap='jet', norm=plt.Normalize(vmin=0, vmax=1))
plt.colorbar(sm)
plt.show()
EACH ORIGINAL MRI IS ANALYZED WITH TWO METHODS: CLASS ACTIVATION MAP (UPPER ROW) AND SALIENCY MAP (LOWER ROW) EACH MAP IS SUPERIMPOSED ON THE ORIGINAL MRI WITH A TRANSPARENCY THAT IS INVERSELY PROPORTIONAL TO THE ESTIMATED PROBABILITY OF THE MRI BELONGING TO THAT CATEGORY (NORMAL MRI, DIFFUSE CORTICAL MALFORMATION, OR PERIVENTRICULAR NODULAR HETEROTOPIA) HIGHER ESTIMATED PROBABILITIES PRODUCE CLEARLY SEEN MAPS OVERLAID ON THE ORIGINAL MRI AND LOWER ESTIMATED PROBABILITIES PRODUCE VERY TRANSPARENT OR NOT APPRECIABLE MAPS OVERLAID ON THE ORIGINAL MRI REAL CLASSIFICATION OF THE IMAGE: PVNH MODEL CLASSIFICATION OF THE IMAGE: Diffuse MCD Prob. Normal MRI: 0.4942 Prob. Diffuse MCD: 0.5013 Prob. PVNH: 0.0046
REAL CLASSIFICATION OF THE IMAGE: PVNH MODEL CLASSIFICATION OF THE IMAGE: Diffuse MCD Prob. Normal MRI: 0.2244 Prob. Diffuse MCD: 0.7703 Prob. PVNH: 0.0053
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 0.9381 Prob. Diffuse MCD: 0.0349 Prob. PVNH: 0.0271
REAL CLASSIFICATION OF THE IMAGE: PVNH MODEL CLASSIFICATION OF THE IMAGE: Diffuse MCD Prob. Normal MRI: 0.0530 Prob. Diffuse MCD: 0.9381 Prob. PVNH: 0.0090
REAL CLASSIFICATION OF THE IMAGE: PVNH MODEL CLASSIFICATION OF THE IMAGE: Diffuse MCD Prob. Normal MRI: 0.0238 Prob. Diffuse MCD: 0.9735 Prob. PVNH: 0.0027
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 1.0000 Prob. Diffuse MCD: 0.0000 Prob. PVNH: 0.0000
REAL CLASSIFICATION OF THE IMAGE: Diffuse MCD MODEL CLASSIFICATION OF THE IMAGE: Diffuse MCD Prob. Normal MRI: 0.0000 Prob. Diffuse MCD: 1.0000 Prob. PVNH: 0.0000
REAL CLASSIFICATION OF THE IMAGE: Diffuse MCD MODEL CLASSIFICATION OF THE IMAGE: Diffuse MCD Prob. Normal MRI: 0.0002 Prob. Diffuse MCD: 0.9997 Prob. PVNH: 0.0000
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 0.9978 Prob. Diffuse MCD: 0.0022 Prob. PVNH: 0.0000
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 1.0000 Prob. Diffuse MCD: 0.0000 Prob. PVNH: 0.0000
REAL CLASSIFICATION OF THE IMAGE: PVNH MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 0.9980 Prob. Diffuse MCD: 0.0018 Prob. PVNH: 0.0003
REAL CLASSIFICATION OF THE IMAGE: PVNH MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 0.9944 Prob. Diffuse MCD: 0.0011 Prob. PVNH: 0.0045
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: PVNH Prob. Normal MRI: 0.0000 Prob. Diffuse MCD: 0.2948 Prob. PVNH: 0.7052
REAL CLASSIFICATION OF THE IMAGE: Diffuse MCD MODEL CLASSIFICATION OF THE IMAGE: Diffuse MCD Prob. Normal MRI: 0.0033 Prob. Diffuse MCD: 0.9930 Prob. PVNH: 0.0036
REAL CLASSIFICATION OF THE IMAGE: Diffuse MCD MODEL CLASSIFICATION OF THE IMAGE: Diffuse MCD Prob. Normal MRI: 0.0000 Prob. Diffuse MCD: 0.8249 Prob. PVNH: 0.1751
REAL CLASSIFICATION OF THE IMAGE: Diffuse MCD MODEL CLASSIFICATION OF THE IMAGE: PVNH Prob. Normal MRI: 0.0000 Prob. Diffuse MCD: 0.0000 Prob. PVNH: 1.0000
REAL CLASSIFICATION OF THE IMAGE: Diffuse MCD MODEL CLASSIFICATION OF THE IMAGE: Diffuse MCD Prob. Normal MRI: 0.0005 Prob. Diffuse MCD: 0.7068 Prob. PVNH: 0.2927
REAL CLASSIFICATION OF THE IMAGE: Diffuse MCD MODEL CLASSIFICATION OF THE IMAGE: PVNH Prob. Normal MRI: 0.0000 Prob. Diffuse MCD: 0.0000 Prob. PVNH: 1.0000
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Diffuse MCD Prob. Normal MRI: 0.0204 Prob. Diffuse MCD: 0.9765 Prob. PVNH: 0.0031
REAL CLASSIFICATION OF THE IMAGE: Diffuse MCD MODEL CLASSIFICATION OF THE IMAGE: Diffuse MCD Prob. Normal MRI: 0.0000 Prob. Diffuse MCD: 1.0000 Prob. PVNH: 0.0000
InceptionV3¶
Define the convolutional neural network¶
In [38]:
# Use InceptionV3 as the base model
base_model = tf.keras.applications.inception_v3.InceptionV3(layers=tf.keras.layers, weights='imagenet', include_top = False, input_shape=train_X.shape[1:])
# Get the output of the base model
x = base_model.output
# Add a 2D global average pooling layer
x = GlobalAveragePooling2D()(x)
## Add the fully-connected layers
x = Dense(units=516, activation='relu')(x)
x = Dropout(rate=0.5)(x)
x = Dense(units=256, activation='relu')(x)
x = Dropout(rate=0.5)(x)
x = Dense(units=64, activation='relu')(x)
# Ad a layer for multiclass classification
predictions = Dense(units = 3, activation = 'softmax')(x)
# Define the model to be trained
model = Model(inputs = base_model.input, outputs = predictions)
# Train only the last 20 layers in the base model
for layer in base_model.layers[:-20]:
layer.trainable = False
for layer in base_model.layers[-20:]:
layer.trainable = True
# Compile the model
opt = Adam(lr = 0.0001)
model.compile(optimizer = opt, loss = 'categorical_crossentropy', metrics = ['accuracy'])
# Fit and test the model in the validation set
historyInceptionV3 = model.fit(shuffled_train_X, shuffled_train_y, validation_data = [shuffled_val_X, shuffled_val_y], epochs = 50, batch_size = 32)
print('\n')
print('\n')
# AUC in train and validation set
auc_trainInceptionV3 = roc_auc_score(shuffled_train_y, model.predict(shuffled_train_X))
print('The AUC in the train set is {:.4f}.'.format(auc_trainInceptionV3))
print('\n')
print('\n')
auc_validInceptionV3 = roc_auc_score(shuffled_val_y, model.predict(shuffled_val_X))
print('The AUC in the validation set is {:.4f}.'.format(auc_validInceptionV3))
print('\n')
print('\n')
print('\n')
print('\n')
# Figure size and colors
mpl.rcParams['figure.figsize'] = (20,24)
plt.rcParams['axes.facecolor']='white'
plt.rcParams['figure.facecolor']='lightgreen'
plt.rcParams['figure.edgecolor']='black'
# Plot history of loss during training
plt.plot(historyInceptionV3.history['loss'], label='Train', color='red')
plt.plot(historyInceptionV3.history['val_loss'], label='Validation', color='blue')
plt.title('Loss in train and validation set', size=30)
plt.xlabel('Epoch', size=24)
plt.ylabel('Categorical cross-entropy loss', size=24)
plt.xticks(fontsize=20)
plt.yticks(fontsize=20)
plt.legend(fontsize=20)
plt.ylim(ymin=0, ymax=5)
plt.grid(b=None)
plt.show()
print('\n')
print('\n')
print('\n')
print('\n')
# Plot history of accuracy
plt.plot(historyInceptionV3.history['accuracy'], label='Train', color='red')
plt.plot(historyInceptionV3.history['val_accuracy'], label='Validation', color='blue')
plt.title('Accuracy in train and validation set', size=30)
plt.xlabel('Epoch', size=24)
plt.ylabel('Accuracy', size=24)
plt.xticks(fontsize=20)
plt.yticks(fontsize=20)
plt.legend(fontsize=20)
plt.ylim(ymin=0, ymax=1.1)
plt.grid(b=None)
plt.show()
Train on 8108 samples, validate on 481 samples Epoch 1/50 8108/8108 [==============================] - 42s 5ms/sample - loss: 0.8371 - accuracy: 0.6164 - val_loss: 1.0116 - val_accuracy: 0.5696 Epoch 2/50 8108/8108 [==============================] - 40s 5ms/sample - loss: 0.4553 - accuracy: 0.8234 - val_loss: 0.8393 - val_accuracy: 0.6902 Epoch 3/50 8108/8108 [==============================] - 41s 5ms/sample - loss: 0.2678 - accuracy: 0.9016 - val_loss: 1.0274 - val_accuracy: 0.6549 Epoch 4/50 8108/8108 [==============================] - 42s 5ms/sample - loss: 0.1593 - accuracy: 0.9424 - val_loss: 1.1886 - val_accuracy: 0.6258 Epoch 5/50 8108/8108 [==============================] - 40s 5ms/sample - loss: 0.1149 - accuracy: 0.9581 - val_loss: 1.3162 - val_accuracy: 0.6611 Epoch 6/50 8108/8108 [==============================] - 37s 5ms/sample - loss: 0.0743 - accuracy: 0.9747 - val_loss: 1.4558 - val_accuracy: 0.6694 Epoch 7/50 8108/8108 [==============================] - 37s 5ms/sample - loss: 0.0607 - accuracy: 0.9792 - val_loss: 1.5263 - val_accuracy: 0.6466 Epoch 8/50 8108/8108 [==============================] - 41s 5ms/sample - loss: 0.0420 - accuracy: 0.9863 - val_loss: 1.5624 - val_accuracy: 0.6653 Epoch 9/50 8108/8108 [==============================] - 36s 4ms/sample - loss: 0.0373 - accuracy: 0.9872 - val_loss: 1.8672 - val_accuracy: 0.6403 Epoch 10/50 8108/8108 [==============================] - 36s 4ms/sample - loss: 0.0329 - accuracy: 0.9904 - val_loss: 1.6060 - val_accuracy: 0.6902 Epoch 11/50 8108/8108 [==============================] - 39s 5ms/sample - loss: 0.0418 - accuracy: 0.9854 - val_loss: 1.9909 - val_accuracy: 0.6091 Epoch 12/50 8108/8108 [==============================] - 38s 5ms/sample - loss: 0.0305 - accuracy: 0.9885 - val_loss: 2.2335 - val_accuracy: 0.6445 Epoch 13/50 8108/8108 [==============================] - 37s 5ms/sample - loss: 0.0244 - accuracy: 0.9919 - val_loss: 1.8852 - val_accuracy: 0.6466 Epoch 14/50 8108/8108 [==============================] - 37s 5ms/sample - loss: 0.0220 - accuracy: 0.9904 - val_loss: 2.1735 - val_accuracy: 0.6403 Epoch 15/50 8108/8108 [==============================] - 37s 5ms/sample - loss: 0.0220 - accuracy: 0.9921 - val_loss: 2.6845 - val_accuracy: 0.5759 Epoch 16/50 8108/8108 [==============================] - 37s 5ms/sample - loss: 0.0173 - accuracy: 0.9933 - val_loss: 2.9636 - val_accuracy: 0.6611 Epoch 17/50 8108/8108 [==============================] - 40s 5ms/sample - loss: 0.0172 - accuracy: 0.9949 - val_loss: 2.2910 - val_accuracy: 0.6590 Epoch 18/50 8108/8108 [==============================] - 37s 5ms/sample - loss: 0.0265 - accuracy: 0.9905 - val_loss: 2.6702 - val_accuracy: 0.5842 Epoch 19/50 8108/8108 [==============================] - 36s 4ms/sample - loss: 0.0293 - accuracy: 0.9899 - val_loss: 2.3735 - val_accuracy: 0.6320 Epoch 20/50 8108/8108 [==============================] - 36s 4ms/sample - loss: 0.0297 - accuracy: 0.9904 - val_loss: 2.2259 - val_accuracy: 0.6299 Epoch 21/50 8108/8108 [==============================] - 37s 5ms/sample - loss: 0.0171 - accuracy: 0.9949 - val_loss: 2.1995 - val_accuracy: 0.6258 Epoch 22/50 8108/8108 [==============================] - 37s 5ms/sample - loss: 0.0178 - accuracy: 0.9936 - val_loss: 2.4971 - val_accuracy: 0.6320 Epoch 23/50 8108/8108 [==============================] - 40s 5ms/sample - loss: 0.0151 - accuracy: 0.9938 - val_loss: 2.5205 - val_accuracy: 0.6486 Epoch 24/50 8108/8108 [==============================] - 37s 5ms/sample - loss: 0.0151 - accuracy: 0.9946 - val_loss: 2.4740 - val_accuracy: 0.6507 Epoch 25/50 8108/8108 [==============================] - 37s 5ms/sample - loss: 0.0123 - accuracy: 0.9961 - val_loss: 2.4607 - val_accuracy: 0.6840 Epoch 26/50 8108/8108 [==============================] - 37s 5ms/sample - loss: 0.0271 - accuracy: 0.9914 - val_loss: 2.2165 - val_accuracy: 0.6528 Epoch 27/50 8108/8108 [==============================] - 37s 5ms/sample - loss: 0.0107 - accuracy: 0.9964 - val_loss: 2.6663 - val_accuracy: 0.6466 Epoch 28/50 8108/8108 [==============================] - 37s 5ms/sample - loss: 0.0167 - accuracy: 0.9946 - val_loss: 2.9330 - val_accuracy: 0.6258 Epoch 29/50 8108/8108 [==============================] - 36s 5ms/sample - loss: 0.0088 - accuracy: 0.9969 - val_loss: 2.5621 - val_accuracy: 0.5925 Epoch 30/50 8108/8108 [==============================] - 37s 5ms/sample - loss: 0.0137 - accuracy: 0.9956 - val_loss: 2.8963 - val_accuracy: 0.6486 Epoch 31/50 8108/8108 [==============================] - 40s 5ms/sample - loss: 0.0117 - accuracy: 0.9964 - val_loss: 2.9313 - val_accuracy: 0.5925 Epoch 32/50 8108/8108 [==============================] - 37s 5ms/sample - loss: 0.0127 - accuracy: 0.9957 - val_loss: 3.5106 - val_accuracy: 0.5593 Epoch 33/50 8108/8108 [==============================] - 36s 4ms/sample - loss: 0.0169 - accuracy: 0.9947 - val_loss: 2.5125 - val_accuracy: 0.6403 Epoch 34/50 8108/8108 [==============================] - 37s 5ms/sample - loss: 0.0113 - accuracy: 0.9963 - val_loss: 3.4000 - val_accuracy: 0.6029 Epoch 35/50 8108/8108 [==============================] - 36s 4ms/sample - loss: 0.0091 - accuracy: 0.9972 - val_loss: 2.1101 - val_accuracy: 0.6944 Epoch 36/50 8108/8108 [==============================] - 37s 5ms/sample - loss: 0.0076 - accuracy: 0.9974 - val_loss: 2.4562 - val_accuracy: 0.6549 Epoch 37/50 8108/8108 [==============================] - 38s 5ms/sample - loss: 0.0148 - accuracy: 0.9949 - val_loss: 2.7961 - val_accuracy: 0.6507 Epoch 38/50 8108/8108 [==============================] - 39s 5ms/sample - loss: 0.0123 - accuracy: 0.9958 - val_loss: 2.8453 - val_accuracy: 0.6757 Epoch 39/50 8108/8108 [==============================] - 37s 5ms/sample - loss: 0.0128 - accuracy: 0.9956 - val_loss: 2.3335 - val_accuracy: 0.6445 Epoch 40/50 8108/8108 [==============================] - 37s 5ms/sample - loss: 0.0154 - accuracy: 0.9951 - val_loss: 2.5405 - val_accuracy: 0.6424 Epoch 41/50 8108/8108 [==============================] - 37s 5ms/sample - loss: 0.0112 - accuracy: 0.9969 - val_loss: 3.1520 - val_accuracy: 0.6424 Epoch 42/50 8108/8108 [==============================] - 37s 5ms/sample - loss: 0.0125 - accuracy: 0.9954 - val_loss: 2.6042 - val_accuracy: 0.6403 Epoch 43/50 8108/8108 [==============================] - 37s 5ms/sample - loss: 0.0140 - accuracy: 0.9957 - val_loss: 2.5549 - val_accuracy: 0.6466 Epoch 44/50 8108/8108 [==============================] - 39s 5ms/sample - loss: 0.0090 - accuracy: 0.9965 - val_loss: 2.8494 - val_accuracy: 0.6362 Epoch 45/50 8108/8108 [==============================] - 38s 5ms/sample - loss: 0.0048 - accuracy: 0.9983 - val_loss: 3.0638 - val_accuracy: 0.6008 Epoch 46/50 8108/8108 [==============================] - 37s 5ms/sample - loss: 0.0196 - accuracy: 0.9930 - val_loss: 2.5955 - val_accuracy: 0.6216 Epoch 47/50 8108/8108 [==============================] - 37s 5ms/sample - loss: 0.0042 - accuracy: 0.9983 - val_loss: 3.0923 - val_accuracy: 0.6195 Epoch 48/50 8108/8108 [==============================] - 37s 5ms/sample - loss: 0.0075 - accuracy: 0.9972 - val_loss: 3.1216 - val_accuracy: 0.6112 Epoch 49/50 8108/8108 [==============================] - 37s 5ms/sample - loss: 0.0142 - accuracy: 0.9956 - val_loss: 3.9253 - val_accuracy: 0.5780 Epoch 50/50 8108/8108 [==============================] - 37s 5ms/sample - loss: 0.0066 - accuracy: 0.9975 - val_loss: 3.6358 - val_accuracy: 0.5551 The AUC in the train set is 1.0000. The AUC in the validation set is 0.8029.
In [39]:
# Generate predictions in the form of probabilities for the validation set
valInceptionV3 = model.predict(shuffled_val_X, batch_size = 32)
# Generate the confusion matrix in the validation set
y_true = np.argmax(shuffled_val_y, axis=1)
y_predInceptionV3 = np.argmax(valInceptionV3, axis=1)
# Confusion matrix
pd.DataFrame(confusion_matrix(y_true, y_predInceptionV3), index=['True: Normal', 'True: Diffuse CM', 'True: PVNH'], columns=['Prediction: Normal', 'Prediction: Diffuse CM', 'Prediction: PVNH']).T
Out[39]:
| True: Normal | True: Diffuse CM | True: PVNH | |
|---|---|---|---|
| Prediction: Normal | 117 | 32 | 70 |
| Prediction: Diffuse CM | 24 | 107 | 62 |
| Prediction: PVNH | 18 | 8 | 43 |
In [40]:
# Calculate accuracy in the validation set
accuracy_InceptionV3 = accuracy_score(y_true=y_true, y_pred=y_predInceptionV3)
print('The accuracy in the validation set is {:.4f}.'.format(accuracy_InceptionV3))
The accuracy in the validation set is 0.5551.
In [41]:
# Calculate AUC in the validation set
auc_validInceptionV3 = roc_auc_score(shuffled_val_y, model.predict(shuffled_val_X))
print('The AUC in the validation set is {:.4f}.'.format(auc_validInceptionV3))
The AUC in the validation set is 0.8029.
In [42]:
# Classification report
print(classification_report(y_true, y_predInceptionV3, target_names=['Normal MRI', 'Diffuse CM', 'PVNH']))
precision recall f1-score support
Normal MRI 0.53 0.74 0.62 159
Diffuse CM 0.55 0.73 0.63 147
PVNH 0.62 0.25 0.35 175
accuracy 0.56 481
macro avg 0.57 0.57 0.53 481
weighted avg 0.57 0.56 0.53 481
Save model InceptionV3¶
In [43]:
# Serialize model to JSON
model_json = model.to_json()
with open("InceptionV3.json", "w") as json_file:
json_file.write(model_json)
# Serialize weights to HDF5
model.save_weights("InceptionV3.h5")
Model visualization¶
In [44]:
# Visualize the structure and layers of the model
model.layers
Out[44]:
[<tensorflow.python.keras.engine.input_layer.InputLayer at 0x7f3120644358>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f3120644550>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f3120644cf8>, <tensorflow.python.keras.layers.core.Activation at 0x7f31206449e8>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f3120644ef0>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f310b0bcb38>, <tensorflow.python.keras.layers.core.Activation at 0x7f310b0bc7b8>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f313c0675c0>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f313c420da0>, <tensorflow.python.keras.layers.core.Activation at 0x7f313c2c31d0>, <tensorflow.python.keras.layers.pooling.MaxPooling2D at 0x7f313c2c3358>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f313c2a2978>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f313c27f438>, 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<tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f310b1baeb8>, <tensorflow.python.keras.layers.core.Activation at 0x7f310b19a908>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f310af23860>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f310b19a048>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f310af15dd8>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f31201a16a0>, <tensorflow.python.keras.layers.core.Activation at 0x7f310af15da0>, <tensorflow.python.keras.layers.core.Activation at 0x7f31201a1c18>, <tensorflow.python.keras.layers.pooling.MaxPooling2D at 0x7f31201a1358>, <tensorflow.python.keras.layers.merge.Concatenate at 0x7f31201a1a20>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f2fc16ad828>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f3114087da0>, <tensorflow.python.keras.layers.core.Activation at 0x7f3114087d68>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f3114099240>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f311408acc0>, <tensorflow.python.keras.layers.core.Activation at 0x7f310af60668>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f31201e5320>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f310af60128>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f310b431e80>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f310af51e80>, <tensorflow.python.keras.layers.core.Activation at 0x7f310b4316a0>, <tensorflow.python.keras.layers.core.Activation at 0x7f310af63978>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f310b431160>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f310af630b8>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f310b41a860>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f31205e7710>, <tensorflow.python.keras.layers.core.Activation at 0x7f310b41add8>, <tensorflow.python.keras.layers.core.Activation at 0x7f31205e7c88>, <tensorflow.python.keras.layers.pooling.AveragePooling2D at 0x7f31201516d8>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f31201f9a58>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f310b41a518>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f31205e73c8>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f3120151da0>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f31201f9e10>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f2fc16adb70>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f3120151a20>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f31201416a0>, <tensorflow.python.keras.layers.core.Activation at 0x7f31201e58d0>, <tensorflow.python.keras.layers.core.Activation at 0x7f2fc16ae128>, <tensorflow.python.keras.layers.core.Activation at 0x7f312013c160>, <tensorflow.python.keras.layers.core.Activation at 0x7f310b2856a0>, <tensorflow.python.keras.layers.merge.Concatenate at 0x7f310b2850f0>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f312003d400>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f3120054da0>, <tensorflow.python.keras.layers.core.Activation at 0x7f312004b748>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f312004b208>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f2fc16c1e80>, <tensorflow.python.keras.layers.core.Activation at 0x7f2fc16f0978>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f310b273320>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f2fc16f00b8>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f310b220b00>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f310b134630>, <tensorflow.python.keras.layers.core.Activation at 0x7f310b21d0b8>, <tensorflow.python.keras.layers.core.Activation at 0x7f310b134ba8>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f310b2207b8>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f310b1342e8>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f310b532d30>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f310b148860>, <tensorflow.python.keras.layers.core.Activation at 0x7f310b532cf8>, <tensorflow.python.keras.layers.core.Activation at 0x7f310b148dd8>, <tensorflow.python.keras.layers.pooling.AveragePooling2D at 0x7f310b35b748>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f310b285400>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f310b5329e8>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f310b148518>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f310b35be10>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f310b273898>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f310b50d320>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f310b35ba90>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f300c098fd0>, <tensorflow.python.keras.layers.core.Activation at 0x7f310b273e80>, <tensorflow.python.keras.layers.core.Activation at 0x7f310b50d2e8>, <tensorflow.python.keras.layers.core.Activation at 0x7f310b385080>, <tensorflow.python.keras.layers.core.Activation at 0x7f300c0c6710>, <tensorflow.python.keras.layers.merge.Concatenate at 0x7f300c0c6160>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f312057c048>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f3120586da0>, <tensorflow.python.keras.layers.core.Activation at 0x7f31205837f0>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f3120583080>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f3120108f28>, <tensorflow.python.keras.layers.core.Activation at 0x7f3120104a20>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f300c0a33c8>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f3120104160>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f3114064ba8>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f310af046d8>, <tensorflow.python.keras.layers.core.Activation at 0x7f3114033160>, <tensorflow.python.keras.layers.core.Activation at 0x7f310af04c50>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f3114064860>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f310af04390>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f310b4dedd8>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f310aed4908>, <tensorflow.python.keras.layers.core.Activation at 0x7f310b4deda0>, <tensorflow.python.keras.layers.core.Activation at 0x7f310aed4ef0>, <tensorflow.python.keras.layers.pooling.AveragePooling2D at 0x7f300c0147f0>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f300c0c63c8>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f310b4d1240>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f310aed45c0>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f300c014eb8>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f300c0a3940>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f310b4d8c18>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f300c014b38>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f310b38cf28>, <tensorflow.python.keras.layers.core.Activation at 0x7f300c0a3f60>, <tensorflow.python.keras.layers.core.Activation at 0x7f310b4d8fd0>, <tensorflow.python.keras.layers.core.Activation at 0x7f300c0360f0>, <tensorflow.python.keras.layers.core.Activation at 0x7f310b3ab7b8>, <tensorflow.python.keras.layers.merge.Concatenate at 0x7f310b3ab208>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f3120727128>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f3120736e48>, <tensorflow.python.keras.layers.core.Activation at 0x7f312071f898>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f312071f128>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f310afd2550>, <tensorflow.python.keras.layers.core.Activation at 0x7f310afd2ac8>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f310b393e80>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f310afd2208>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f310b046c50>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f310aeaf780>, <tensorflow.python.keras.layers.core.Activation at 0x7f310b046c18>, <tensorflow.python.keras.layers.core.Activation at 0x7f310aeafcf8>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f310b046e80>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f310aeaf438>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f31140e5e80>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f310b2a09b0>, <tensorflow.python.keras.layers.core.Activation at 0x7f31140e5e48>, <tensorflow.python.keras.layers.core.Activation at 0x7f310b2a0f98>, <tensorflow.python.keras.layers.pooling.AveragePooling2D at 0x7f310b2c6898>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f310b3ab470>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f31140b8320>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f310b2a0668>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f310b2c6e10>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f310b393780>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f3120709cc0>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f310b2c6be0>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f310b1d8fd0>, <tensorflow.python.keras.layers.core.Activation at 0x7f310b00a128>, <tensorflow.python.keras.layers.core.Activation at 0x7f3120727668>, <tensorflow.python.keras.layers.core.Activation at 0x7f310b2b9198>, <tensorflow.python.keras.layers.core.Activation at 0x7f310b1da860>, <tensorflow.python.keras.layers.merge.Concatenate at 0x7f310b1da2b0>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f31203d3f98>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f310b33def0>, <tensorflow.python.keras.layers.core.Activation at 0x7f310b33deb8>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f310b333390>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f310b14fd30>, <tensorflow.python.keras.layers.core.Activation at 0x7f310b1686d8>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f310b1da5c0>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f310b168198>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f31203eaa58>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f310b14beb8>, <tensorflow.python.keras.layers.core.Activation at 0x7f31203e7048>, <tensorflow.python.keras.layers.core.Activation at 0x7f31206f7908>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f31203ea4e0>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f31206f7048>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f31203d3cc0>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f31206d45c0>, <tensorflow.python.keras.layers.core.Activation at 0x7f31203d3c88>, <tensorflow.python.keras.layers.core.Activation at 0x7f31206d4b38>, <tensorflow.python.keras.layers.pooling.MaxPooling2D at 0x7f31206d4278>, <tensorflow.python.keras.layers.merge.Concatenate at 0x7f31206d4940>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f310b2f5b70>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f310b0e1e80>, <tensorflow.python.keras.layers.core.Activation at 0x7f310b0e1e48>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f310b4af160>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f310b0ea2e8>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f310b453eb8>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f310b04acf8>, <tensorflow.python.keras.layers.core.Activation at 0x7f310b45d908>, <tensorflow.python.keras.layers.core.Activation at 0x7f310b0536a0>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f310b45d048>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f310b2ca320>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f310b053160>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f310b080160>, <tensorflow.python.keras.layers.pooling.AveragePooling2D at 0x7f310afac908>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f310b4a9898>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f310b2ca588>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f310b2f57f0>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f310b077e80>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f310afac588>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f300c055780>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f310b4a9c50>, <tensorflow.python.keras.layers.core.Activation at 0x7f310b2cab70>, <tensorflow.python.keras.layers.core.Activation at 0x7f310b2f5d68>, <tensorflow.python.keras.layers.core.Activation at 0x7f310b0808d0>, <tensorflow.python.keras.layers.core.Activation at 0x7f310afacb00>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f300c055ef0>, <tensorflow.python.keras.layers.core.Activation at 0x7f310b4af710>, <tensorflow.python.keras.layers.merge.Concatenate at 0x7f310b2f54a8>, <tensorflow.python.keras.layers.merge.Concatenate at 0x7f310afac240>, <tensorflow.python.keras.layers.core.Activation at 0x7f300c05d6a0>, <tensorflow.python.keras.layers.merge.Concatenate at 0x7f300c05d0f0>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f2fc1584358>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f2fc15ace48>, <tensorflow.python.keras.layers.core.Activation at 0x7f2fc15b3c50>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f2fc15fd358>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f2fc15b3160>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f2fc1628b38>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f2fc155f8d0>, <tensorflow.python.keras.layers.core.Activation at 0x7f2fc16310f0>, <tensorflow.python.keras.layers.core.Activation at 0x7f2fc155fe48>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f2fc16287f0>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f2fc15d4a20>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f2fc155f588>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f2fc150c7b8>, <tensorflow.python.keras.layers.pooling.AveragePooling2D at 0x7f2fc14c01d0>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f300c05d358>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f2fc15d4d68>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f2fc1580f98>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f2fc150cb00>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f2fc1537d30>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f2fc14e2a58>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f2fc15fd8d0>, <tensorflow.python.keras.layers.core.Activation at 0x7f2fc15d4d30>, <tensorflow.python.keras.layers.core.Activation at 0x7f2fc1580f60>, <tensorflow.python.keras.layers.core.Activation at 0x7f2fc15150b8>, <tensorflow.python.keras.layers.core.Activation at 0x7f2fc1537cf8>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f2fc14eb860>, <tensorflow.python.keras.layers.core.Activation at 0x7f2fc15fdeb8>, <tensorflow.python.keras.layers.merge.Concatenate at 0x7f2fc1584400>, <tensorflow.python.keras.layers.merge.Concatenate at 0x7f2fc15379e8>, <tensorflow.python.keras.layers.core.Activation at 0x7f2fc14ebe48>, <tensorflow.python.keras.layers.merge.Concatenate at 0x7f2fc14eb4a8>, <tensorflow.python.keras.layers.pooling.GlobalAveragePooling2D at 0x7f2fc1457518>, <tensorflow.python.keras.layers.core.Dense at 0x7f3120573f60>, <tensorflow.python.keras.layers.core.Dropout at 0x7f2fc14575c0>, <tensorflow.python.keras.layers.core.Dense at 0x7f2fc1422e48>, <tensorflow.python.keras.layers.core.Dropout at 0x7f2fc1422940>, <tensorflow.python.keras.layers.core.Dense at 0x7f2fc140d0f0>, <tensorflow.python.keras.layers.core.Dense at 0x7f2fc142d780>]
In [45]:
# Visualize the structure and layers of the model
print(model.summary())
Model: "model_61"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_2 (InputLayer) [(None, 512, 512, 3) 0
__________________________________________________________________________________________________
conv2d_35 (Conv2D) (None, 255, 255, 32) 864 input_2[0][0]
__________________________________________________________________________________________________
batch_normalization_4 (BatchNor (None, 255, 255, 32) 96 conv2d_35[0][0]
__________________________________________________________________________________________________
activation_4 (Activation) (None, 255, 255, 32) 0 batch_normalization_4[0][0]
__________________________________________________________________________________________________
conv2d_36 (Conv2D) (None, 253, 253, 32) 9216 activation_4[0][0]
__________________________________________________________________________________________________
batch_normalization_5 (BatchNor (None, 253, 253, 32) 96 conv2d_36[0][0]
__________________________________________________________________________________________________
activation_5 (Activation) (None, 253, 253, 32) 0 batch_normalization_5[0][0]
__________________________________________________________________________________________________
conv2d_37 (Conv2D) (None, 253, 253, 64) 18432 activation_5[0][0]
__________________________________________________________________________________________________
batch_normalization_6 (BatchNor (None, 253, 253, 64) 192 conv2d_37[0][0]
__________________________________________________________________________________________________
activation_6 (Activation) (None, 253, 253, 64) 0 batch_normalization_6[0][0]
__________________________________________________________________________________________________
max_pooling2d_10 (MaxPooling2D) (None, 126, 126, 64) 0 activation_6[0][0]
__________________________________________________________________________________________________
conv2d_38 (Conv2D) (None, 126, 126, 80) 5120 max_pooling2d_10[0][0]
__________________________________________________________________________________________________
batch_normalization_7 (BatchNor (None, 126, 126, 80) 240 conv2d_38[0][0]
__________________________________________________________________________________________________
activation_7 (Activation) (None, 126, 126, 80) 0 batch_normalization_7[0][0]
__________________________________________________________________________________________________
conv2d_39 (Conv2D) (None, 124, 124, 192 138240 activation_7[0][0]
__________________________________________________________________________________________________
batch_normalization_8 (BatchNor (None, 124, 124, 192 576 conv2d_39[0][0]
__________________________________________________________________________________________________
activation_8 (Activation) (None, 124, 124, 192 0 batch_normalization_8[0][0]
__________________________________________________________________________________________________
max_pooling2d_11 (MaxPooling2D) (None, 61, 61, 192) 0 activation_8[0][0]
__________________________________________________________________________________________________
conv2d_43 (Conv2D) (None, 61, 61, 64) 12288 max_pooling2d_11[0][0]
__________________________________________________________________________________________________
batch_normalization_12 (BatchNo (None, 61, 61, 64) 192 conv2d_43[0][0]
__________________________________________________________________________________________________
activation_12 (Activation) (None, 61, 61, 64) 0 batch_normalization_12[0][0]
__________________________________________________________________________________________________
conv2d_41 (Conv2D) (None, 61, 61, 48) 9216 max_pooling2d_11[0][0]
__________________________________________________________________________________________________
conv2d_44 (Conv2D) (None, 61, 61, 96) 55296 activation_12[0][0]
__________________________________________________________________________________________________
batch_normalization_10 (BatchNo (None, 61, 61, 48) 144 conv2d_41[0][0]
__________________________________________________________________________________________________
batch_normalization_13 (BatchNo (None, 61, 61, 96) 288 conv2d_44[0][0]
__________________________________________________________________________________________________
activation_10 (Activation) (None, 61, 61, 48) 0 batch_normalization_10[0][0]
__________________________________________________________________________________________________
activation_13 (Activation) (None, 61, 61, 96) 0 batch_normalization_13[0][0]
__________________________________________________________________________________________________
average_pooling2d (AveragePooli (None, 61, 61, 192) 0 max_pooling2d_11[0][0]
__________________________________________________________________________________________________
conv2d_40 (Conv2D) (None, 61, 61, 64) 12288 max_pooling2d_11[0][0]
__________________________________________________________________________________________________
conv2d_42 (Conv2D) (None, 61, 61, 64) 76800 activation_10[0][0]
__________________________________________________________________________________________________
conv2d_45 (Conv2D) (None, 61, 61, 96) 82944 activation_13[0][0]
__________________________________________________________________________________________________
conv2d_46 (Conv2D) (None, 61, 61, 32) 6144 average_pooling2d[0][0]
__________________________________________________________________________________________________
batch_normalization_9 (BatchNor (None, 61, 61, 64) 192 conv2d_40[0][0]
__________________________________________________________________________________________________
batch_normalization_11 (BatchNo (None, 61, 61, 64) 192 conv2d_42[0][0]
__________________________________________________________________________________________________
batch_normalization_14 (BatchNo (None, 61, 61, 96) 288 conv2d_45[0][0]
__________________________________________________________________________________________________
batch_normalization_15 (BatchNo (None, 61, 61, 32) 96 conv2d_46[0][0]
__________________________________________________________________________________________________
activation_9 (Activation) (None, 61, 61, 64) 0 batch_normalization_9[0][0]
__________________________________________________________________________________________________
activation_11 (Activation) (None, 61, 61, 64) 0 batch_normalization_11[0][0]
__________________________________________________________________________________________________
activation_14 (Activation) (None, 61, 61, 96) 0 batch_normalization_14[0][0]
__________________________________________________________________________________________________
activation_15 (Activation) (None, 61, 61, 32) 0 batch_normalization_15[0][0]
__________________________________________________________________________________________________
mixed0 (Concatenate) (None, 61, 61, 256) 0 activation_9[0][0]
activation_11[0][0]
activation_14[0][0]
activation_15[0][0]
__________________________________________________________________________________________________
conv2d_50 (Conv2D) (None, 61, 61, 64) 16384 mixed0[0][0]
__________________________________________________________________________________________________
batch_normalization_19 (BatchNo (None, 61, 61, 64) 192 conv2d_50[0][0]
__________________________________________________________________________________________________
activation_19 (Activation) (None, 61, 61, 64) 0 batch_normalization_19[0][0]
__________________________________________________________________________________________________
conv2d_48 (Conv2D) (None, 61, 61, 48) 12288 mixed0[0][0]
__________________________________________________________________________________________________
conv2d_51 (Conv2D) (None, 61, 61, 96) 55296 activation_19[0][0]
__________________________________________________________________________________________________
batch_normalization_17 (BatchNo (None, 61, 61, 48) 144 conv2d_48[0][0]
__________________________________________________________________________________________________
batch_normalization_20 (BatchNo (None, 61, 61, 96) 288 conv2d_51[0][0]
__________________________________________________________________________________________________
activation_17 (Activation) (None, 61, 61, 48) 0 batch_normalization_17[0][0]
__________________________________________________________________________________________________
activation_20 (Activation) (None, 61, 61, 96) 0 batch_normalization_20[0][0]
__________________________________________________________________________________________________
average_pooling2d_1 (AveragePoo (None, 61, 61, 256) 0 mixed0[0][0]
__________________________________________________________________________________________________
conv2d_47 (Conv2D) (None, 61, 61, 64) 16384 mixed0[0][0]
__________________________________________________________________________________________________
conv2d_49 (Conv2D) (None, 61, 61, 64) 76800 activation_17[0][0]
__________________________________________________________________________________________________
conv2d_52 (Conv2D) (None, 61, 61, 96) 82944 activation_20[0][0]
__________________________________________________________________________________________________
conv2d_53 (Conv2D) (None, 61, 61, 64) 16384 average_pooling2d_1[0][0]
__________________________________________________________________________________________________
batch_normalization_16 (BatchNo (None, 61, 61, 64) 192 conv2d_47[0][0]
__________________________________________________________________________________________________
batch_normalization_18 (BatchNo (None, 61, 61, 64) 192 conv2d_49[0][0]
__________________________________________________________________________________________________
batch_normalization_21 (BatchNo (None, 61, 61, 96) 288 conv2d_52[0][0]
__________________________________________________________________________________________________
batch_normalization_22 (BatchNo (None, 61, 61, 64) 192 conv2d_53[0][0]
__________________________________________________________________________________________________
activation_16 (Activation) (None, 61, 61, 64) 0 batch_normalization_16[0][0]
__________________________________________________________________________________________________
activation_18 (Activation) (None, 61, 61, 64) 0 batch_normalization_18[0][0]
__________________________________________________________________________________________________
activation_21 (Activation) (None, 61, 61, 96) 0 batch_normalization_21[0][0]
__________________________________________________________________________________________________
activation_22 (Activation) (None, 61, 61, 64) 0 batch_normalization_22[0][0]
__________________________________________________________________________________________________
mixed1 (Concatenate) (None, 61, 61, 288) 0 activation_16[0][0]
activation_18[0][0]
activation_21[0][0]
activation_22[0][0]
__________________________________________________________________________________________________
conv2d_57 (Conv2D) (None, 61, 61, 64) 18432 mixed1[0][0]
__________________________________________________________________________________________________
batch_normalization_26 (BatchNo (None, 61, 61, 64) 192 conv2d_57[0][0]
__________________________________________________________________________________________________
activation_26 (Activation) (None, 61, 61, 64) 0 batch_normalization_26[0][0]
__________________________________________________________________________________________________
conv2d_55 (Conv2D) (None, 61, 61, 48) 13824 mixed1[0][0]
__________________________________________________________________________________________________
conv2d_58 (Conv2D) (None, 61, 61, 96) 55296 activation_26[0][0]
__________________________________________________________________________________________________
batch_normalization_24 (BatchNo (None, 61, 61, 48) 144 conv2d_55[0][0]
__________________________________________________________________________________________________
batch_normalization_27 (BatchNo (None, 61, 61, 96) 288 conv2d_58[0][0]
__________________________________________________________________________________________________
activation_24 (Activation) (None, 61, 61, 48) 0 batch_normalization_24[0][0]
__________________________________________________________________________________________________
activation_27 (Activation) (None, 61, 61, 96) 0 batch_normalization_27[0][0]
__________________________________________________________________________________________________
average_pooling2d_2 (AveragePoo (None, 61, 61, 288) 0 mixed1[0][0]
__________________________________________________________________________________________________
conv2d_54 (Conv2D) (None, 61, 61, 64) 18432 mixed1[0][0]
__________________________________________________________________________________________________
conv2d_56 (Conv2D) (None, 61, 61, 64) 76800 activation_24[0][0]
__________________________________________________________________________________________________
conv2d_59 (Conv2D) (None, 61, 61, 96) 82944 activation_27[0][0]
__________________________________________________________________________________________________
conv2d_60 (Conv2D) (None, 61, 61, 64) 18432 average_pooling2d_2[0][0]
__________________________________________________________________________________________________
batch_normalization_23 (BatchNo (None, 61, 61, 64) 192 conv2d_54[0][0]
__________________________________________________________________________________________________
batch_normalization_25 (BatchNo (None, 61, 61, 64) 192 conv2d_56[0][0]
__________________________________________________________________________________________________
batch_normalization_28 (BatchNo (None, 61, 61, 96) 288 conv2d_59[0][0]
__________________________________________________________________________________________________
batch_normalization_29 (BatchNo (None, 61, 61, 64) 192 conv2d_60[0][0]
__________________________________________________________________________________________________
activation_23 (Activation) (None, 61, 61, 64) 0 batch_normalization_23[0][0]
__________________________________________________________________________________________________
activation_25 (Activation) (None, 61, 61, 64) 0 batch_normalization_25[0][0]
__________________________________________________________________________________________________
activation_28 (Activation) (None, 61, 61, 96) 0 batch_normalization_28[0][0]
__________________________________________________________________________________________________
activation_29 (Activation) (None, 61, 61, 64) 0 batch_normalization_29[0][0]
__________________________________________________________________________________________________
mixed2 (Concatenate) (None, 61, 61, 288) 0 activation_23[0][0]
activation_25[0][0]
activation_28[0][0]
activation_29[0][0]
__________________________________________________________________________________________________
conv2d_62 (Conv2D) (None, 61, 61, 64) 18432 mixed2[0][0]
__________________________________________________________________________________________________
batch_normalization_31 (BatchNo (None, 61, 61, 64) 192 conv2d_62[0][0]
__________________________________________________________________________________________________
activation_31 (Activation) (None, 61, 61, 64) 0 batch_normalization_31[0][0]
__________________________________________________________________________________________________
conv2d_63 (Conv2D) (None, 61, 61, 96) 55296 activation_31[0][0]
__________________________________________________________________________________________________
batch_normalization_32 (BatchNo (None, 61, 61, 96) 288 conv2d_63[0][0]
__________________________________________________________________________________________________
activation_32 (Activation) (None, 61, 61, 96) 0 batch_normalization_32[0][0]
__________________________________________________________________________________________________
conv2d_61 (Conv2D) (None, 30, 30, 384) 995328 mixed2[0][0]
__________________________________________________________________________________________________
conv2d_64 (Conv2D) (None, 30, 30, 96) 82944 activation_32[0][0]
__________________________________________________________________________________________________
batch_normalization_30 (BatchNo (None, 30, 30, 384) 1152 conv2d_61[0][0]
__________________________________________________________________________________________________
batch_normalization_33 (BatchNo (None, 30, 30, 96) 288 conv2d_64[0][0]
__________________________________________________________________________________________________
activation_30 (Activation) (None, 30, 30, 384) 0 batch_normalization_30[0][0]
__________________________________________________________________________________________________
activation_33 (Activation) (None, 30, 30, 96) 0 batch_normalization_33[0][0]
__________________________________________________________________________________________________
max_pooling2d_12 (MaxPooling2D) (None, 30, 30, 288) 0 mixed2[0][0]
__________________________________________________________________________________________________
mixed3 (Concatenate) (None, 30, 30, 768) 0 activation_30[0][0]
activation_33[0][0]
max_pooling2d_12[0][0]
__________________________________________________________________________________________________
conv2d_69 (Conv2D) (None, 30, 30, 128) 98304 mixed3[0][0]
__________________________________________________________________________________________________
batch_normalization_38 (BatchNo (None, 30, 30, 128) 384 conv2d_69[0][0]
__________________________________________________________________________________________________
activation_38 (Activation) (None, 30, 30, 128) 0 batch_normalization_38[0][0]
__________________________________________________________________________________________________
conv2d_70 (Conv2D) (None, 30, 30, 128) 114688 activation_38[0][0]
__________________________________________________________________________________________________
batch_normalization_39 (BatchNo (None, 30, 30, 128) 384 conv2d_70[0][0]
__________________________________________________________________________________________________
activation_39 (Activation) (None, 30, 30, 128) 0 batch_normalization_39[0][0]
__________________________________________________________________________________________________
conv2d_66 (Conv2D) (None, 30, 30, 128) 98304 mixed3[0][0]
__________________________________________________________________________________________________
conv2d_71 (Conv2D) (None, 30, 30, 128) 114688 activation_39[0][0]
__________________________________________________________________________________________________
batch_normalization_35 (BatchNo (None, 30, 30, 128) 384 conv2d_66[0][0]
__________________________________________________________________________________________________
batch_normalization_40 (BatchNo (None, 30, 30, 128) 384 conv2d_71[0][0]
__________________________________________________________________________________________________
activation_35 (Activation) (None, 30, 30, 128) 0 batch_normalization_35[0][0]
__________________________________________________________________________________________________
activation_40 (Activation) (None, 30, 30, 128) 0 batch_normalization_40[0][0]
__________________________________________________________________________________________________
conv2d_67 (Conv2D) (None, 30, 30, 128) 114688 activation_35[0][0]
__________________________________________________________________________________________________
conv2d_72 (Conv2D) (None, 30, 30, 128) 114688 activation_40[0][0]
__________________________________________________________________________________________________
batch_normalization_36 (BatchNo (None, 30, 30, 128) 384 conv2d_67[0][0]
__________________________________________________________________________________________________
batch_normalization_41 (BatchNo (None, 30, 30, 128) 384 conv2d_72[0][0]
__________________________________________________________________________________________________
activation_36 (Activation) (None, 30, 30, 128) 0 batch_normalization_36[0][0]
__________________________________________________________________________________________________
activation_41 (Activation) (None, 30, 30, 128) 0 batch_normalization_41[0][0]
__________________________________________________________________________________________________
average_pooling2d_3 (AveragePoo (None, 30, 30, 768) 0 mixed3[0][0]
__________________________________________________________________________________________________
conv2d_65 (Conv2D) (None, 30, 30, 192) 147456 mixed3[0][0]
__________________________________________________________________________________________________
conv2d_68 (Conv2D) (None, 30, 30, 192) 172032 activation_36[0][0]
__________________________________________________________________________________________________
conv2d_73 (Conv2D) (None, 30, 30, 192) 172032 activation_41[0][0]
__________________________________________________________________________________________________
conv2d_74 (Conv2D) (None, 30, 30, 192) 147456 average_pooling2d_3[0][0]
__________________________________________________________________________________________________
batch_normalization_34 (BatchNo (None, 30, 30, 192) 576 conv2d_65[0][0]
__________________________________________________________________________________________________
batch_normalization_37 (BatchNo (None, 30, 30, 192) 576 conv2d_68[0][0]
__________________________________________________________________________________________________
batch_normalization_42 (BatchNo (None, 30, 30, 192) 576 conv2d_73[0][0]
__________________________________________________________________________________________________
batch_normalization_43 (BatchNo (None, 30, 30, 192) 576 conv2d_74[0][0]
__________________________________________________________________________________________________
activation_34 (Activation) (None, 30, 30, 192) 0 batch_normalization_34[0][0]
__________________________________________________________________________________________________
activation_37 (Activation) (None, 30, 30, 192) 0 batch_normalization_37[0][0]
__________________________________________________________________________________________________
activation_42 (Activation) (None, 30, 30, 192) 0 batch_normalization_42[0][0]
__________________________________________________________________________________________________
activation_43 (Activation) (None, 30, 30, 192) 0 batch_normalization_43[0][0]
__________________________________________________________________________________________________
mixed4 (Concatenate) (None, 30, 30, 768) 0 activation_34[0][0]
activation_37[0][0]
activation_42[0][0]
activation_43[0][0]
__________________________________________________________________________________________________
conv2d_79 (Conv2D) (None, 30, 30, 160) 122880 mixed4[0][0]
__________________________________________________________________________________________________
batch_normalization_48 (BatchNo (None, 30, 30, 160) 480 conv2d_79[0][0]
__________________________________________________________________________________________________
activation_48 (Activation) (None, 30, 30, 160) 0 batch_normalization_48[0][0]
__________________________________________________________________________________________________
conv2d_80 (Conv2D) (None, 30, 30, 160) 179200 activation_48[0][0]
__________________________________________________________________________________________________
batch_normalization_49 (BatchNo (None, 30, 30, 160) 480 conv2d_80[0][0]
__________________________________________________________________________________________________
activation_49 (Activation) (None, 30, 30, 160) 0 batch_normalization_49[0][0]
__________________________________________________________________________________________________
conv2d_76 (Conv2D) (None, 30, 30, 160) 122880 mixed4[0][0]
__________________________________________________________________________________________________
conv2d_81 (Conv2D) (None, 30, 30, 160) 179200 activation_49[0][0]
__________________________________________________________________________________________________
batch_normalization_45 (BatchNo (None, 30, 30, 160) 480 conv2d_76[0][0]
__________________________________________________________________________________________________
batch_normalization_50 (BatchNo (None, 30, 30, 160) 480 conv2d_81[0][0]
__________________________________________________________________________________________________
activation_45 (Activation) (None, 30, 30, 160) 0 batch_normalization_45[0][0]
__________________________________________________________________________________________________
activation_50 (Activation) (None, 30, 30, 160) 0 batch_normalization_50[0][0]
__________________________________________________________________________________________________
conv2d_77 (Conv2D) (None, 30, 30, 160) 179200 activation_45[0][0]
__________________________________________________________________________________________________
conv2d_82 (Conv2D) (None, 30, 30, 160) 179200 activation_50[0][0]
__________________________________________________________________________________________________
batch_normalization_46 (BatchNo (None, 30, 30, 160) 480 conv2d_77[0][0]
__________________________________________________________________________________________________
batch_normalization_51 (BatchNo (None, 30, 30, 160) 480 conv2d_82[0][0]
__________________________________________________________________________________________________
activation_46 (Activation) (None, 30, 30, 160) 0 batch_normalization_46[0][0]
__________________________________________________________________________________________________
activation_51 (Activation) (None, 30, 30, 160) 0 batch_normalization_51[0][0]
__________________________________________________________________________________________________
average_pooling2d_4 (AveragePoo (None, 30, 30, 768) 0 mixed4[0][0]
__________________________________________________________________________________________________
conv2d_75 (Conv2D) (None, 30, 30, 192) 147456 mixed4[0][0]
__________________________________________________________________________________________________
conv2d_78 (Conv2D) (None, 30, 30, 192) 215040 activation_46[0][0]
__________________________________________________________________________________________________
conv2d_83 (Conv2D) (None, 30, 30, 192) 215040 activation_51[0][0]
__________________________________________________________________________________________________
conv2d_84 (Conv2D) (None, 30, 30, 192) 147456 average_pooling2d_4[0][0]
__________________________________________________________________________________________________
batch_normalization_44 (BatchNo (None, 30, 30, 192) 576 conv2d_75[0][0]
__________________________________________________________________________________________________
batch_normalization_47 (BatchNo (None, 30, 30, 192) 576 conv2d_78[0][0]
__________________________________________________________________________________________________
batch_normalization_52 (BatchNo (None, 30, 30, 192) 576 conv2d_83[0][0]
__________________________________________________________________________________________________
batch_normalization_53 (BatchNo (None, 30, 30, 192) 576 conv2d_84[0][0]
__________________________________________________________________________________________________
activation_44 (Activation) (None, 30, 30, 192) 0 batch_normalization_44[0][0]
__________________________________________________________________________________________________
activation_47 (Activation) (None, 30, 30, 192) 0 batch_normalization_47[0][0]
__________________________________________________________________________________________________
activation_52 (Activation) (None, 30, 30, 192) 0 batch_normalization_52[0][0]
__________________________________________________________________________________________________
activation_53 (Activation) (None, 30, 30, 192) 0 batch_normalization_53[0][0]
__________________________________________________________________________________________________
mixed5 (Concatenate) (None, 30, 30, 768) 0 activation_44[0][0]
activation_47[0][0]
activation_52[0][0]
activation_53[0][0]
__________________________________________________________________________________________________
conv2d_89 (Conv2D) (None, 30, 30, 160) 122880 mixed5[0][0]
__________________________________________________________________________________________________
batch_normalization_58 (BatchNo (None, 30, 30, 160) 480 conv2d_89[0][0]
__________________________________________________________________________________________________
activation_58 (Activation) (None, 30, 30, 160) 0 batch_normalization_58[0][0]
__________________________________________________________________________________________________
conv2d_90 (Conv2D) (None, 30, 30, 160) 179200 activation_58[0][0]
__________________________________________________________________________________________________
batch_normalization_59 (BatchNo (None, 30, 30, 160) 480 conv2d_90[0][0]
__________________________________________________________________________________________________
activation_59 (Activation) (None, 30, 30, 160) 0 batch_normalization_59[0][0]
__________________________________________________________________________________________________
conv2d_86 (Conv2D) (None, 30, 30, 160) 122880 mixed5[0][0]
__________________________________________________________________________________________________
conv2d_91 (Conv2D) (None, 30, 30, 160) 179200 activation_59[0][0]
__________________________________________________________________________________________________
batch_normalization_55 (BatchNo (None, 30, 30, 160) 480 conv2d_86[0][0]
__________________________________________________________________________________________________
batch_normalization_60 (BatchNo (None, 30, 30, 160) 480 conv2d_91[0][0]
__________________________________________________________________________________________________
activation_55 (Activation) (None, 30, 30, 160) 0 batch_normalization_55[0][0]
__________________________________________________________________________________________________
activation_60 (Activation) (None, 30, 30, 160) 0 batch_normalization_60[0][0]
__________________________________________________________________________________________________
conv2d_87 (Conv2D) (None, 30, 30, 160) 179200 activation_55[0][0]
__________________________________________________________________________________________________
conv2d_92 (Conv2D) (None, 30, 30, 160) 179200 activation_60[0][0]
__________________________________________________________________________________________________
batch_normalization_56 (BatchNo (None, 30, 30, 160) 480 conv2d_87[0][0]
__________________________________________________________________________________________________
batch_normalization_61 (BatchNo (None, 30, 30, 160) 480 conv2d_92[0][0]
__________________________________________________________________________________________________
activation_56 (Activation) (None, 30, 30, 160) 0 batch_normalization_56[0][0]
__________________________________________________________________________________________________
activation_61 (Activation) (None, 30, 30, 160) 0 batch_normalization_61[0][0]
__________________________________________________________________________________________________
average_pooling2d_5 (AveragePoo (None, 30, 30, 768) 0 mixed5[0][0]
__________________________________________________________________________________________________
conv2d_85 (Conv2D) (None, 30, 30, 192) 147456 mixed5[0][0]
__________________________________________________________________________________________________
conv2d_88 (Conv2D) (None, 30, 30, 192) 215040 activation_56[0][0]
__________________________________________________________________________________________________
conv2d_93 (Conv2D) (None, 30, 30, 192) 215040 activation_61[0][0]
__________________________________________________________________________________________________
conv2d_94 (Conv2D) (None, 30, 30, 192) 147456 average_pooling2d_5[0][0]
__________________________________________________________________________________________________
batch_normalization_54 (BatchNo (None, 30, 30, 192) 576 conv2d_85[0][0]
__________________________________________________________________________________________________
batch_normalization_57 (BatchNo (None, 30, 30, 192) 576 conv2d_88[0][0]
__________________________________________________________________________________________________
batch_normalization_62 (BatchNo (None, 30, 30, 192) 576 conv2d_93[0][0]
__________________________________________________________________________________________________
batch_normalization_63 (BatchNo (None, 30, 30, 192) 576 conv2d_94[0][0]
__________________________________________________________________________________________________
activation_54 (Activation) (None, 30, 30, 192) 0 batch_normalization_54[0][0]
__________________________________________________________________________________________________
activation_57 (Activation) (None, 30, 30, 192) 0 batch_normalization_57[0][0]
__________________________________________________________________________________________________
activation_62 (Activation) (None, 30, 30, 192) 0 batch_normalization_62[0][0]
__________________________________________________________________________________________________
activation_63 (Activation) (None, 30, 30, 192) 0 batch_normalization_63[0][0]
__________________________________________________________________________________________________
mixed6 (Concatenate) (None, 30, 30, 768) 0 activation_54[0][0]
activation_57[0][0]
activation_62[0][0]
activation_63[0][0]
__________________________________________________________________________________________________
conv2d_99 (Conv2D) (None, 30, 30, 192) 147456 mixed6[0][0]
__________________________________________________________________________________________________
batch_normalization_68 (BatchNo (None, 30, 30, 192) 576 conv2d_99[0][0]
__________________________________________________________________________________________________
activation_68 (Activation) (None, 30, 30, 192) 0 batch_normalization_68[0][0]
__________________________________________________________________________________________________
conv2d_100 (Conv2D) (None, 30, 30, 192) 258048 activation_68[0][0]
__________________________________________________________________________________________________
batch_normalization_69 (BatchNo (None, 30, 30, 192) 576 conv2d_100[0][0]
__________________________________________________________________________________________________
activation_69 (Activation) (None, 30, 30, 192) 0 batch_normalization_69[0][0]
__________________________________________________________________________________________________
conv2d_96 (Conv2D) (None, 30, 30, 192) 147456 mixed6[0][0]
__________________________________________________________________________________________________
conv2d_101 (Conv2D) (None, 30, 30, 192) 258048 activation_69[0][0]
__________________________________________________________________________________________________
batch_normalization_65 (BatchNo (None, 30, 30, 192) 576 conv2d_96[0][0]
__________________________________________________________________________________________________
batch_normalization_70 (BatchNo (None, 30, 30, 192) 576 conv2d_101[0][0]
__________________________________________________________________________________________________
activation_65 (Activation) (None, 30, 30, 192) 0 batch_normalization_65[0][0]
__________________________________________________________________________________________________
activation_70 (Activation) (None, 30, 30, 192) 0 batch_normalization_70[0][0]
__________________________________________________________________________________________________
conv2d_97 (Conv2D) (None, 30, 30, 192) 258048 activation_65[0][0]
__________________________________________________________________________________________________
conv2d_102 (Conv2D) (None, 30, 30, 192) 258048 activation_70[0][0]
__________________________________________________________________________________________________
batch_normalization_66 (BatchNo (None, 30, 30, 192) 576 conv2d_97[0][0]
__________________________________________________________________________________________________
batch_normalization_71 (BatchNo (None, 30, 30, 192) 576 conv2d_102[0][0]
__________________________________________________________________________________________________
activation_66 (Activation) (None, 30, 30, 192) 0 batch_normalization_66[0][0]
__________________________________________________________________________________________________
activation_71 (Activation) (None, 30, 30, 192) 0 batch_normalization_71[0][0]
__________________________________________________________________________________________________
average_pooling2d_6 (AveragePoo (None, 30, 30, 768) 0 mixed6[0][0]
__________________________________________________________________________________________________
conv2d_95 (Conv2D) (None, 30, 30, 192) 147456 mixed6[0][0]
__________________________________________________________________________________________________
conv2d_98 (Conv2D) (None, 30, 30, 192) 258048 activation_66[0][0]
__________________________________________________________________________________________________
conv2d_103 (Conv2D) (None, 30, 30, 192) 258048 activation_71[0][0]
__________________________________________________________________________________________________
conv2d_104 (Conv2D) (None, 30, 30, 192) 147456 average_pooling2d_6[0][0]
__________________________________________________________________________________________________
batch_normalization_64 (BatchNo (None, 30, 30, 192) 576 conv2d_95[0][0]
__________________________________________________________________________________________________
batch_normalization_67 (BatchNo (None, 30, 30, 192) 576 conv2d_98[0][0]
__________________________________________________________________________________________________
batch_normalization_72 (BatchNo (None, 30, 30, 192) 576 conv2d_103[0][0]
__________________________________________________________________________________________________
batch_normalization_73 (BatchNo (None, 30, 30, 192) 576 conv2d_104[0][0]
__________________________________________________________________________________________________
activation_64 (Activation) (None, 30, 30, 192) 0 batch_normalization_64[0][0]
__________________________________________________________________________________________________
activation_67 (Activation) (None, 30, 30, 192) 0 batch_normalization_67[0][0]
__________________________________________________________________________________________________
activation_72 (Activation) (None, 30, 30, 192) 0 batch_normalization_72[0][0]
__________________________________________________________________________________________________
activation_73 (Activation) (None, 30, 30, 192) 0 batch_normalization_73[0][0]
__________________________________________________________________________________________________
mixed7 (Concatenate) (None, 30, 30, 768) 0 activation_64[0][0]
activation_67[0][0]
activation_72[0][0]
activation_73[0][0]
__________________________________________________________________________________________________
conv2d_107 (Conv2D) (None, 30, 30, 192) 147456 mixed7[0][0]
__________________________________________________________________________________________________
batch_normalization_76 (BatchNo (None, 30, 30, 192) 576 conv2d_107[0][0]
__________________________________________________________________________________________________
activation_76 (Activation) (None, 30, 30, 192) 0 batch_normalization_76[0][0]
__________________________________________________________________________________________________
conv2d_108 (Conv2D) (None, 30, 30, 192) 258048 activation_76[0][0]
__________________________________________________________________________________________________
batch_normalization_77 (BatchNo (None, 30, 30, 192) 576 conv2d_108[0][0]
__________________________________________________________________________________________________
activation_77 (Activation) (None, 30, 30, 192) 0 batch_normalization_77[0][0]
__________________________________________________________________________________________________
conv2d_105 (Conv2D) (None, 30, 30, 192) 147456 mixed7[0][0]
__________________________________________________________________________________________________
conv2d_109 (Conv2D) (None, 30, 30, 192) 258048 activation_77[0][0]
__________________________________________________________________________________________________
batch_normalization_74 (BatchNo (None, 30, 30, 192) 576 conv2d_105[0][0]
__________________________________________________________________________________________________
batch_normalization_78 (BatchNo (None, 30, 30, 192) 576 conv2d_109[0][0]
__________________________________________________________________________________________________
activation_74 (Activation) (None, 30, 30, 192) 0 batch_normalization_74[0][0]
__________________________________________________________________________________________________
activation_78 (Activation) (None, 30, 30, 192) 0 batch_normalization_78[0][0]
__________________________________________________________________________________________________
conv2d_106 (Conv2D) (None, 14, 14, 320) 552960 activation_74[0][0]
__________________________________________________________________________________________________
conv2d_110 (Conv2D) (None, 14, 14, 192) 331776 activation_78[0][0]
__________________________________________________________________________________________________
batch_normalization_75 (BatchNo (None, 14, 14, 320) 960 conv2d_106[0][0]
__________________________________________________________________________________________________
batch_normalization_79 (BatchNo (None, 14, 14, 192) 576 conv2d_110[0][0]
__________________________________________________________________________________________________
activation_75 (Activation) (None, 14, 14, 320) 0 batch_normalization_75[0][0]
__________________________________________________________________________________________________
activation_79 (Activation) (None, 14, 14, 192) 0 batch_normalization_79[0][0]
__________________________________________________________________________________________________
max_pooling2d_13 (MaxPooling2D) (None, 14, 14, 768) 0 mixed7[0][0]
__________________________________________________________________________________________________
mixed8 (Concatenate) (None, 14, 14, 1280) 0 activation_75[0][0]
activation_79[0][0]
max_pooling2d_13[0][0]
__________________________________________________________________________________________________
conv2d_115 (Conv2D) (None, 14, 14, 448) 573440 mixed8[0][0]
__________________________________________________________________________________________________
batch_normalization_84 (BatchNo (None, 14, 14, 448) 1344 conv2d_115[0][0]
__________________________________________________________________________________________________
activation_84 (Activation) (None, 14, 14, 448) 0 batch_normalization_84[0][0]
__________________________________________________________________________________________________
conv2d_112 (Conv2D) (None, 14, 14, 384) 491520 mixed8[0][0]
__________________________________________________________________________________________________
conv2d_116 (Conv2D) (None, 14, 14, 384) 1548288 activation_84[0][0]
__________________________________________________________________________________________________
batch_normalization_81 (BatchNo (None, 14, 14, 384) 1152 conv2d_112[0][0]
__________________________________________________________________________________________________
batch_normalization_85 (BatchNo (None, 14, 14, 384) 1152 conv2d_116[0][0]
__________________________________________________________________________________________________
activation_81 (Activation) (None, 14, 14, 384) 0 batch_normalization_81[0][0]
__________________________________________________________________________________________________
activation_85 (Activation) (None, 14, 14, 384) 0 batch_normalization_85[0][0]
__________________________________________________________________________________________________
conv2d_113 (Conv2D) (None, 14, 14, 384) 442368 activation_81[0][0]
__________________________________________________________________________________________________
conv2d_114 (Conv2D) (None, 14, 14, 384) 442368 activation_81[0][0]
__________________________________________________________________________________________________
conv2d_117 (Conv2D) (None, 14, 14, 384) 442368 activation_85[0][0]
__________________________________________________________________________________________________
conv2d_118 (Conv2D) (None, 14, 14, 384) 442368 activation_85[0][0]
__________________________________________________________________________________________________
average_pooling2d_7 (AveragePoo (None, 14, 14, 1280) 0 mixed8[0][0]
__________________________________________________________________________________________________
conv2d_111 (Conv2D) (None, 14, 14, 320) 409600 mixed8[0][0]
__________________________________________________________________________________________________
batch_normalization_82 (BatchNo (None, 14, 14, 384) 1152 conv2d_113[0][0]
__________________________________________________________________________________________________
batch_normalization_83 (BatchNo (None, 14, 14, 384) 1152 conv2d_114[0][0]
__________________________________________________________________________________________________
batch_normalization_86 (BatchNo (None, 14, 14, 384) 1152 conv2d_117[0][0]
__________________________________________________________________________________________________
batch_normalization_87 (BatchNo (None, 14, 14, 384) 1152 conv2d_118[0][0]
__________________________________________________________________________________________________
conv2d_119 (Conv2D) (None, 14, 14, 192) 245760 average_pooling2d_7[0][0]
__________________________________________________________________________________________________
batch_normalization_80 (BatchNo (None, 14, 14, 320) 960 conv2d_111[0][0]
__________________________________________________________________________________________________
activation_82 (Activation) (None, 14, 14, 384) 0 batch_normalization_82[0][0]
__________________________________________________________________________________________________
activation_83 (Activation) (None, 14, 14, 384) 0 batch_normalization_83[0][0]
__________________________________________________________________________________________________
activation_86 (Activation) (None, 14, 14, 384) 0 batch_normalization_86[0][0]
__________________________________________________________________________________________________
activation_87 (Activation) (None, 14, 14, 384) 0 batch_normalization_87[0][0]
__________________________________________________________________________________________________
batch_normalization_88 (BatchNo (None, 14, 14, 192) 576 conv2d_119[0][0]
__________________________________________________________________________________________________
activation_80 (Activation) (None, 14, 14, 320) 0 batch_normalization_80[0][0]
__________________________________________________________________________________________________
mixed9_0 (Concatenate) (None, 14, 14, 768) 0 activation_82[0][0]
activation_83[0][0]
__________________________________________________________________________________________________
concatenate_5 (Concatenate) (None, 14, 14, 768) 0 activation_86[0][0]
activation_87[0][0]
__________________________________________________________________________________________________
activation_88 (Activation) (None, 14, 14, 192) 0 batch_normalization_88[0][0]
__________________________________________________________________________________________________
mixed9 (Concatenate) (None, 14, 14, 2048) 0 activation_80[0][0]
mixed9_0[0][0]
concatenate_5[0][0]
activation_88[0][0]
__________________________________________________________________________________________________
conv2d_124 (Conv2D) (None, 14, 14, 448) 917504 mixed9[0][0]
__________________________________________________________________________________________________
batch_normalization_93 (BatchNo (None, 14, 14, 448) 1344 conv2d_124[0][0]
__________________________________________________________________________________________________
activation_93 (Activation) (None, 14, 14, 448) 0 batch_normalization_93[0][0]
__________________________________________________________________________________________________
conv2d_121 (Conv2D) (None, 14, 14, 384) 786432 mixed9[0][0]
__________________________________________________________________________________________________
conv2d_125 (Conv2D) (None, 14, 14, 384) 1548288 activation_93[0][0]
__________________________________________________________________________________________________
batch_normalization_90 (BatchNo (None, 14, 14, 384) 1152 conv2d_121[0][0]
__________________________________________________________________________________________________
batch_normalization_94 (BatchNo (None, 14, 14, 384) 1152 conv2d_125[0][0]
__________________________________________________________________________________________________
activation_90 (Activation) (None, 14, 14, 384) 0 batch_normalization_90[0][0]
__________________________________________________________________________________________________
activation_94 (Activation) (None, 14, 14, 384) 0 batch_normalization_94[0][0]
__________________________________________________________________________________________________
conv2d_122 (Conv2D) (None, 14, 14, 384) 442368 activation_90[0][0]
__________________________________________________________________________________________________
conv2d_123 (Conv2D) (None, 14, 14, 384) 442368 activation_90[0][0]
__________________________________________________________________________________________________
conv2d_126 (Conv2D) (None, 14, 14, 384) 442368 activation_94[0][0]
__________________________________________________________________________________________________
conv2d_127 (Conv2D) (None, 14, 14, 384) 442368 activation_94[0][0]
__________________________________________________________________________________________________
average_pooling2d_8 (AveragePoo (None, 14, 14, 2048) 0 mixed9[0][0]
__________________________________________________________________________________________________
conv2d_120 (Conv2D) (None, 14, 14, 320) 655360 mixed9[0][0]
__________________________________________________________________________________________________
batch_normalization_91 (BatchNo (None, 14, 14, 384) 1152 conv2d_122[0][0]
__________________________________________________________________________________________________
batch_normalization_92 (BatchNo (None, 14, 14, 384) 1152 conv2d_123[0][0]
__________________________________________________________________________________________________
batch_normalization_95 (BatchNo (None, 14, 14, 384) 1152 conv2d_126[0][0]
__________________________________________________________________________________________________
batch_normalization_96 (BatchNo (None, 14, 14, 384) 1152 conv2d_127[0][0]
__________________________________________________________________________________________________
conv2d_128 (Conv2D) (None, 14, 14, 192) 393216 average_pooling2d_8[0][0]
__________________________________________________________________________________________________
batch_normalization_89 (BatchNo (None, 14, 14, 320) 960 conv2d_120[0][0]
__________________________________________________________________________________________________
activation_91 (Activation) (None, 14, 14, 384) 0 batch_normalization_91[0][0]
__________________________________________________________________________________________________
activation_92 (Activation) (None, 14, 14, 384) 0 batch_normalization_92[0][0]
__________________________________________________________________________________________________
activation_95 (Activation) (None, 14, 14, 384) 0 batch_normalization_95[0][0]
__________________________________________________________________________________________________
activation_96 (Activation) (None, 14, 14, 384) 0 batch_normalization_96[0][0]
__________________________________________________________________________________________________
batch_normalization_97 (BatchNo (None, 14, 14, 192) 576 conv2d_128[0][0]
__________________________________________________________________________________________________
activation_89 (Activation) (None, 14, 14, 320) 0 batch_normalization_89[0][0]
__________________________________________________________________________________________________
mixed9_1 (Concatenate) (None, 14, 14, 768) 0 activation_91[0][0]
activation_92[0][0]
__________________________________________________________________________________________________
concatenate_6 (Concatenate) (None, 14, 14, 768) 0 activation_95[0][0]
activation_96[0][0]
__________________________________________________________________________________________________
activation_97 (Activation) (None, 14, 14, 192) 0 batch_normalization_97[0][0]
__________________________________________________________________________________________________
mixed10 (Concatenate) (None, 14, 14, 2048) 0 activation_89[0][0]
mixed9_1[0][0]
concatenate_6[0][0]
activation_97[0][0]
__________________________________________________________________________________________________
global_average_pooling2d_1 (Glo (None, 2048) 0 mixed10[0][0]
__________________________________________________________________________________________________
dense_4 (Dense) (None, 516) 1057284 global_average_pooling2d_1[0][0]
__________________________________________________________________________________________________
dropout_2 (Dropout) (None, 516) 0 dense_4[0][0]
__________________________________________________________________________________________________
dense_5 (Dense) (None, 256) 132352 dropout_2[0][0]
__________________________________________________________________________________________________
dropout_3 (Dropout) (None, 256) 0 dense_5[0][0]
__________________________________________________________________________________________________
dense_6 (Dense) (None, 64) 16448 dropout_3[0][0]
__________________________________________________________________________________________________
dense_7 (Dense) (None, 3) 195 dense_6[0][0]
==================================================================================================
Total params: 23,009,063
Trainable params: 3,141,639
Non-trainable params: 19,867,424
__________________________________________________________________________________________________
None
In [46]:
# Define modifier to replace the sigmoid function of the last layer to a linear function
def model_modifier(m):
m.layers[-1].activation = tf.keras.activations.linear
# Define losses functions. 0 is the index for a normal MRI
loss_normal = lambda output: K.mean(output[:, 0])
# Define losses functions. 1 is the index for a diffuse malformation of cortical development MRI
loss_diffuseMCD = lambda output: K.mean(output[:, 1])
# Define losses functions. 2 is the index for a PVNH MRI
loss_PVNH = lambda output: K.mean(output[:, 2])
# Create Gradcam object
gradcam = Gradcam(model, model_modifier)
# Create Saliency object
saliency = Saliency(model, model_modifier)
# Iterate through the MRIs in test set
# Set background to white color
plt.rcParams['axes.facecolor']='white'
plt.rcParams['figure.facecolor']='white'
plt.rcParams['figure.edgecolor']='white'
print('\n \n' + '\033[1m' + 'EACH ORIGINAL MRI IS ANALYZED WITH TWO METHODS: CLASS ACTIVATION MAP (UPPER ROW) AND SALIENCY MAP (LOWER ROW)' + '\033[0m' + '\n')
print('\033[1m' + 'EACH MAP IS SUPERIMPOSED ON THE ORIGINAL MRI WITH A TRANSPARENCY THAT IS INVERSELY PROPORTIONAL TO THE ESTIMATED PROBABILITY OF THE MRI BELONGING TO THAT CATEGORY (NORMAL MRI, DIFFUSE CORTICAL MALFORMATION, OR PERIVENTRICULAR NODULAR HETEROTOPIA) \n \nHIGHER ESTIMATED PROBABILITIES PRODUCE CLEARLY SEEN MAPS OVERLAID ON THE ORIGINAL MRI AND LOWER ESTIMATED PROBABILITIES PRODUCE VERY TRANSPARENT OR NOT APPRECIABLE MAPS OVERLAID ON THE ORIGINAL MRI' + '\033[0m'+ '\n')
for i in range(20):
# Print spaces to separate from the next image
print('\n \n \n \n \n \n')
# Print real classification of the image
if y_true[i]==0:
real_classification='Normal MRI'
elif y_true[i]==1:
real_classification='Diffuse MCD'
else:
real_classification='PVNH'
print('\033[1m' + 'REAL CLASSIFICATION OF THE IMAGE: {}'.format(real_classification) + '\033[0m')
# Print model classification and model probability of MCD
if y_predInceptionV3[i]==0:
predicted_classification='Normal MRI'
elif y_predInceptionV3[i]==1:
predicted_classification='Diffuse MCD'
else:
predicted_classification='PVNH'
print('\033[1m' + 'MODEL CLASSIFICATION OF THE IMAGE: {}'.format(predicted_classification) + '\033[0m \n')
print('\033[1m' + ' Prob. Normal MRI: {:.4f} '.format(valInceptionV3[i][0]) + 'Prob. Diffuse MCD: {:.4f} '.format(valInceptionV3[i][1]), 'Prob. PVNH: {:.4f}'.format(valInceptionV3[i][2]) + '\033[0m')
# Arrays to plot
original_image=shuffled_val_X[i]
list_heatmaps=[
# GradCam heatmap for normal MRI
normalize(gradcam(loss_normal, shuffled_val_X[i])),
# GradCam heatmap for diffuse MCD
normalize(gradcam(loss_diffuseMCD, shuffled_val_X[i])),
# GradCam heatmap for PVNH
normalize(gradcam(loss_PVNH, shuffled_val_X[i])),
# Saliency heatmap for normal MRI
normalize(saliency(loss_normal, seed_input=np.expand_dims(shuffled_val_X[i], axis=0), smooth_noise=0.2)),
# Saliency heatmap for diffuse MCD
normalize(saliency(loss_diffuseMCD, seed_input=np.expand_dims(shuffled_val_X[i], axis=0), smooth_noise=0.2)),
# Saliency heatmap for PVNH
normalize(saliency(loss_PVNH, seed_input=np.expand_dims(shuffled_val_X[i], axis=0), smooth_noise=0.2))
]
# Define figure
f=plt.figure(figsize=(20, 8))
# Define the image grid
grid = ImageGrid(f, 111,
nrows_ncols=(2, 3),
axes_pad=0.05,
share_all=True,
cbar_location="right",
cbar_mode=None,
cbar_size="2%",
cbar_pad=0.15)
# Iterate over the graphs
for j, axis in enumerate(grid):
# Plot original
im=axis.imshow(original_image)
im=axis.imshow(list_heatmaps[j][0], cmap='jet', alpha=0.5*valInceptionV3[i][j%3])
im=axis.set_xticks([])
im=axis.set_yticks([])
# Create scalarmappable for obtaining the colorbar from 0 to 1
sm = plt.cm.ScalarMappable(cmap='jet', norm=plt.Normalize(vmin=0, vmax=1))
plt.colorbar(sm)
plt.show()
EACH ORIGINAL MRI IS ANALYZED WITH TWO METHODS: CLASS ACTIVATION MAP (UPPER ROW) AND SALIENCY MAP (LOWER ROW) EACH MAP IS SUPERIMPOSED ON THE ORIGINAL MRI WITH A TRANSPARENCY THAT IS INVERSELY PROPORTIONAL TO THE ESTIMATED PROBABILITY OF THE MRI BELONGING TO THAT CATEGORY (NORMAL MRI, DIFFUSE CORTICAL MALFORMATION, OR PERIVENTRICULAR NODULAR HETEROTOPIA) HIGHER ESTIMATED PROBABILITIES PRODUCE CLEARLY SEEN MAPS OVERLAID ON THE ORIGINAL MRI AND LOWER ESTIMATED PROBABILITIES PRODUCE VERY TRANSPARENT OR NOT APPRECIABLE MAPS OVERLAID ON THE ORIGINAL MRI REAL CLASSIFICATION OF THE IMAGE: PVNH MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 0.9834 Prob. Diffuse MCD: 0.0161 Prob. PVNH: 0.0005
REAL CLASSIFICATION OF THE IMAGE: PVNH MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 0.9974 Prob. Diffuse MCD: 0.0010 Prob. PVNH: 0.0015
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Diffuse MCD Prob. Normal MRI: 0.0040 Prob. Diffuse MCD: 0.9949 Prob. PVNH: 0.0011
REAL CLASSIFICATION OF THE IMAGE: PVNH MODEL CLASSIFICATION OF THE IMAGE: PVNH Prob. Normal MRI: 0.0092 Prob. Diffuse MCD: 0.2654 Prob. PVNH: 0.7255
REAL CLASSIFICATION OF THE IMAGE: PVNH MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 0.9998 Prob. Diffuse MCD: 0.0002 Prob. PVNH: 0.0000
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 1.0000 Prob. Diffuse MCD: 0.0000 Prob. PVNH: 0.0000
REAL CLASSIFICATION OF THE IMAGE: Diffuse MCD MODEL CLASSIFICATION OF THE IMAGE: Diffuse MCD Prob. Normal MRI: 0.0000 Prob. Diffuse MCD: 0.9994 Prob. PVNH: 0.0006
REAL CLASSIFICATION OF THE IMAGE: Diffuse MCD MODEL CLASSIFICATION OF THE IMAGE: Diffuse MCD Prob. Normal MRI: 0.0000 Prob. Diffuse MCD: 1.0000 Prob. PVNH: 0.0000
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 1.0000 Prob. Diffuse MCD: 0.0000 Prob. PVNH: 0.0000
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 1.0000 Prob. Diffuse MCD: 0.0000 Prob. PVNH: 0.0000
REAL CLASSIFICATION OF THE IMAGE: PVNH MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 0.9905 Prob. Diffuse MCD: 0.0065 Prob. PVNH: 0.0029
REAL CLASSIFICATION OF THE IMAGE: PVNH MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 0.8498 Prob. Diffuse MCD: 0.1444 Prob. PVNH: 0.0058
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Diffuse MCD Prob. Normal MRI: 0.0004 Prob. Diffuse MCD: 0.9996 Prob. PVNH: 0.0000
REAL CLASSIFICATION OF THE IMAGE: Diffuse MCD MODEL CLASSIFICATION OF THE IMAGE: Diffuse MCD Prob. Normal MRI: 0.0000 Prob. Diffuse MCD: 1.0000 Prob. PVNH: 0.0000
REAL CLASSIFICATION OF THE IMAGE: Diffuse MCD MODEL CLASSIFICATION OF THE IMAGE: PVNH Prob. Normal MRI: 0.0000 Prob. Diffuse MCD: 0.0002 Prob. PVNH: 0.9998
REAL CLASSIFICATION OF THE IMAGE: Diffuse MCD MODEL CLASSIFICATION OF THE IMAGE: Diffuse MCD Prob. Normal MRI: 0.0000 Prob. Diffuse MCD: 0.9986 Prob. PVNH: 0.0014
REAL CLASSIFICATION OF THE IMAGE: Diffuse MCD MODEL CLASSIFICATION OF THE IMAGE: Diffuse MCD Prob. Normal MRI: 0.0000 Prob. Diffuse MCD: 1.0000 Prob. PVNH: 0.0000
REAL CLASSIFICATION OF THE IMAGE: Diffuse MCD MODEL CLASSIFICATION OF THE IMAGE: Diffuse MCD Prob. Normal MRI: 0.0000 Prob. Diffuse MCD: 1.0000 Prob. PVNH: 0.0000
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Diffuse MCD Prob. Normal MRI: 0.0000 Prob. Diffuse MCD: 1.0000 Prob. PVNH: 0.0000
REAL CLASSIFICATION OF THE IMAGE: Diffuse MCD MODEL CLASSIFICATION OF THE IMAGE: Diffuse MCD Prob. Normal MRI: 0.0000 Prob. Diffuse MCD: 1.0000 Prob. PVNH: 0.0000
ResNet50¶
Define the convolutional neural network¶
In [47]:
# Use ResNet50 as the base model
base_model = tf.keras.applications.resnet50.ResNet50(layers=tf.keras.layers, weights='imagenet', include_top = False, input_shape=train_X.shape[1:])
# Get the output of the base model
x = base_model.output
# Add a 2D global average pooling layer
x = GlobalAveragePooling2D()(x)
## Add the fully-connected layers
x = Dense(units=516, activation='relu')(x)
x = Dropout(rate=0.5)(x)
x = Dense(units=256, activation='relu')(x)
x = Dropout(rate=0.5)(x)
x = Dense(units=64, activation='relu')(x)
# Ad a layer for multiclass classification
predictions = Dense(units = 3, activation = 'softmax')(x)
# Define the model to be trained
model = Model(inputs = base_model.input, outputs = predictions)
# Train the last 20 layers in the base model
for layer in base_model.layers[:-20]:
layer.trainable = False
for layer in base_model.layers[-20:]:
layer.trainable = True
# Compile the model
opt = Adam(lr = 0.0001)
model.compile(optimizer = opt, loss = 'categorical_crossentropy', metrics = ['accuracy'])
# Fit and test the model in the validation set
historyResNet50 = model.fit(shuffled_train_X, shuffled_train_y, validation_data = [shuffled_val_X, shuffled_val_y], epochs = 50, batch_size = 32)
print('\n')
print('\n')
# AUC in train and validation set
auc_trainResNet50 = roc_auc_score(shuffled_train_y, model.predict(shuffled_train_X))
print('The AUC in the train set is {:.4f}.'.format(auc_trainResNet50))
print('\n')
print('\n')
auc_validResNet50 = roc_auc_score(shuffled_val_y, model.predict(shuffled_val_X))
print('The AUC in the validation set is {:.4f}.'.format(auc_validResNet50))
print('\n')
print('\n')
print('\n')
print('\n')
# Figure size and colors
mpl.rcParams['figure.figsize'] = (20,24)
plt.rcParams['axes.facecolor']='white'
plt.rcParams['figure.facecolor']='lightgreen'
plt.rcParams['figure.edgecolor']='black'
# Plot history of loss during training
plt.plot(historyResNet50.history['loss'], label='Train', color='red')
plt.plot(historyResNet50.history['val_loss'], label='Validation', color='blue')
plt.title('Loss in train and validation set', size=30)
plt.xlabel('Epoch', size=24)
plt.ylabel('Categorical cross-entropy loss', size=24)
plt.xticks(fontsize=20)
plt.yticks(fontsize=20)
plt.legend(fontsize=20)
plt.ylim(ymin=0, ymax=5)
plt.grid(b=None)
plt.show()
print('\n')
print('\n')
print('\n')
print('\n')
# Plot history of accuracy
plt.plot(historyResNet50.history['accuracy'], label='Train', color='red')
plt.plot(historyResNet50.history['val_accuracy'], label='Validation', color='blue')
plt.title('Accuracy in train and validation set', size=30)
plt.xlabel('Epoch', size=24)
plt.ylabel('Accuracy', size=24)
plt.xticks(fontsize=20)
plt.yticks(fontsize=20)
plt.legend(fontsize=20)
plt.ylim(ymin=0, ymax=1.1)
plt.grid(b=None)
plt.show()
Train on 8108 samples, validate on 481 samples Epoch 1/50 8108/8108 [==============================] - 51s 6ms/sample - loss: 1.0255 - accuracy: 0.4663 - val_loss: 0.9939 - val_accuracy: 0.5218 Epoch 2/50 8108/8108 [==============================] - 46s 6ms/sample - loss: 0.8383 - accuracy: 0.6084 - val_loss: 1.2956 - val_accuracy: 0.4116 Epoch 3/50 8108/8108 [==============================] - 52s 6ms/sample - loss: 0.7407 - accuracy: 0.6654 - val_loss: 1.2298 - val_accuracy: 0.5385 Epoch 4/50 8108/8108 [==============================] - 47s 6ms/sample - loss: 0.6518 - accuracy: 0.7269 - val_loss: 1.0720 - val_accuracy: 0.4969 Epoch 5/50 8108/8108 [==============================] - 47s 6ms/sample - loss: 0.5799 - accuracy: 0.7602 - val_loss: 5.7659 - val_accuracy: 0.4387 Epoch 6/50 8108/8108 [==============================] - 47s 6ms/sample - loss: 0.5087 - accuracy: 0.7940 - val_loss: 1.2793 - val_accuracy: 0.5073 Epoch 7/50 8108/8108 [==============================] - 50s 6ms/sample - loss: 0.4570 - accuracy: 0.8222 - val_loss: 5.1467 - val_accuracy: 0.3721 Epoch 8/50 8108/8108 [==============================] - 47s 6ms/sample - loss: 0.3856 - accuracy: 0.8529 - val_loss: 2.2167 - val_accuracy: 0.4532 Epoch 9/50 8108/8108 [==============================] - 47s 6ms/sample - loss: 0.3407 - accuracy: 0.8709 - val_loss: 3.2033 - val_accuracy: 0.4699 Epoch 10/50 8108/8108 [==============================] - 47s 6ms/sample - loss: 0.3134 - accuracy: 0.8767 - val_loss: 1.5844 - val_accuracy: 0.5468 Epoch 11/50 8108/8108 [==============================] - 47s 6ms/sample - loss: 0.2564 - accuracy: 0.9054 - val_loss: 1.3802 - val_accuracy: 0.6320 Epoch 12/50 8108/8108 [==============================] - 51s 6ms/sample - loss: 0.2273 - accuracy: 0.9121 - val_loss: 2.2166 - val_accuracy: 0.4948 Epoch 13/50 8108/8108 [==============================] - 47s 6ms/sample - loss: 0.1970 - accuracy: 0.9266 - val_loss: 1.6565 - val_accuracy: 0.6154 Epoch 14/50 8108/8108 [==============================] - 47s 6ms/sample - loss: 0.1637 - accuracy: 0.9391 - val_loss: 3.6866 - val_accuracy: 0.4366 Epoch 15/50 8108/8108 [==============================] - 47s 6ms/sample - loss: 0.1734 - accuracy: 0.9345 - val_loss: 2.6621 - val_accuracy: 0.4304 Epoch 16/50 8108/8108 [==============================] - 49s 6ms/sample - loss: 0.1475 - accuracy: 0.9478 - val_loss: 1.9873 - val_accuracy: 0.6112 Epoch 17/50 8108/8108 [==============================] - 49s 6ms/sample - loss: 0.1320 - accuracy: 0.9551 - val_loss: 2.2144 - val_accuracy: 0.5946 Epoch 18/50 8108/8108 [==============================] - 46s 6ms/sample - loss: 0.1076 - accuracy: 0.9620 - val_loss: 6.4756 - val_accuracy: 0.4283 Epoch 19/50 8108/8108 [==============================] - 46s 6ms/sample - loss: 0.1327 - accuracy: 0.9540 - val_loss: 1.6808 - val_accuracy: 0.5489 Epoch 20/50 8108/8108 [==============================] - 50s 6ms/sample - loss: 0.1020 - accuracy: 0.9656 - val_loss: 2.7403 - val_accuracy: 0.5821 Epoch 21/50 8108/8108 [==============================] - 47s 6ms/sample - loss: 0.0928 - accuracy: 0.9676 - val_loss: 1.8666 - val_accuracy: 0.6424 Epoch 22/50 8108/8108 [==============================] - 47s 6ms/sample - loss: 0.0846 - accuracy: 0.9719 - val_loss: 3.2685 - val_accuracy: 0.5073 Epoch 23/50 8108/8108 [==============================] - 51s 6ms/sample - loss: 0.0899 - accuracy: 0.9683 - val_loss: 2.7032 - val_accuracy: 0.4324 Epoch 24/50 8108/8108 [==============================] - 47s 6ms/sample - loss: 0.0817 - accuracy: 0.9710 - val_loss: 2.4362 - val_accuracy: 0.5613 Epoch 25/50 8108/8108 [==============================] - 47s 6ms/sample - loss: 0.0733 - accuracy: 0.9735 - val_loss: 14.8234 - val_accuracy: 0.3451 Epoch 26/50 8108/8108 [==============================] - 47s 6ms/sample - loss: 0.0600 - accuracy: 0.9790 - val_loss: 3.4563 - val_accuracy: 0.5405 Epoch 27/50 8108/8108 [==============================] - 50s 6ms/sample - loss: 0.0591 - accuracy: 0.9800 - val_loss: 2.5701 - val_accuracy: 0.5780 Epoch 28/50 8108/8108 [==============================] - 48s 6ms/sample - loss: 0.0619 - accuracy: 0.9784 - val_loss: 2.0870 - val_accuracy: 0.5676 Epoch 29/50 8108/8108 [==============================] - 47s 6ms/sample - loss: 0.0634 - accuracy: 0.9784 - val_loss: 3.4210 - val_accuracy: 0.4886 Epoch 30/50 8108/8108 [==============================] - 47s 6ms/sample - loss: 0.0476 - accuracy: 0.9829 - val_loss: 2.8726 - val_accuracy: 0.5031 Epoch 31/50 8108/8108 [==============================] - 47s 6ms/sample - loss: 0.0687 - accuracy: 0.9782 - val_loss: 3.0613 - val_accuracy: 0.4782 Epoch 32/50 8108/8108 [==============================] - 47s 6ms/sample - loss: 0.0577 - accuracy: 0.9801 - val_loss: 1.5425 - val_accuracy: 0.6757 Epoch 33/50 8108/8108 [==============================] - 49s 6ms/sample - loss: 0.0539 - accuracy: 0.9827 - val_loss: 3.6115 - val_accuracy: 0.5094 Epoch 34/50 8108/8108 [==============================] - 47s 6ms/sample - loss: 0.0477 - accuracy: 0.9837 - val_loss: 1.8065 - val_accuracy: 0.6757 Epoch 35/50 8108/8108 [==============================] - 47s 6ms/sample - loss: 0.0471 - accuracy: 0.9826 - val_loss: 2.1610 - val_accuracy: 0.6154 Epoch 36/50 8108/8108 [==============================] - 47s 6ms/sample - loss: 0.0513 - accuracy: 0.9826 - val_loss: 3.3357 - val_accuracy: 0.5489 Epoch 37/50 8108/8108 [==============================] - 51s 6ms/sample - loss: 0.0482 - accuracy: 0.9830 - val_loss: 3.0112 - val_accuracy: 0.5322 Epoch 38/50 8108/8108 [==============================] - 47s 6ms/sample - loss: 0.0434 - accuracy: 0.9873 - val_loss: 2.0698 - val_accuracy: 0.5322 Epoch 39/50 8108/8108 [==============================] - 47s 6ms/sample - loss: 0.0252 - accuracy: 0.9926 - val_loss: 3.9333 - val_accuracy: 0.5884 Epoch 40/50 8108/8108 [==============================] - 47s 6ms/sample - loss: 0.0424 - accuracy: 0.9838 - val_loss: 2.3058 - val_accuracy: 0.6341 Epoch 41/50 8108/8108 [==============================] - 47s 6ms/sample - loss: 0.0615 - accuracy: 0.9809 - val_loss: 9.5753 - val_accuracy: 0.4137 Epoch 42/50 8108/8108 [==============================] - 50s 6ms/sample - loss: 0.0503 - accuracy: 0.9833 - val_loss: 6.6045 - val_accuracy: 0.3534 Epoch 43/50 8108/8108 [==============================] - 48s 6ms/sample - loss: 0.0324 - accuracy: 0.9894 - val_loss: 3.5444 - val_accuracy: 0.4449 Epoch 44/50 8108/8108 [==============================] - 47s 6ms/sample - loss: 0.0360 - accuracy: 0.9898 - val_loss: 15.0606 - val_accuracy: 0.3909 Epoch 45/50 8108/8108 [==============================] - 47s 6ms/sample - loss: 0.0444 - accuracy: 0.9850 - val_loss: 2.4176 - val_accuracy: 0.6362 Epoch 46/50 8108/8108 [==============================] - 50s 6ms/sample - loss: 0.0389 - accuracy: 0.9878 - val_loss: 2.9852 - val_accuracy: 0.5509 Epoch 47/50 8108/8108 [==============================] - 48s 6ms/sample - loss: 0.0322 - accuracy: 0.9899 - val_loss: 2.5703 - val_accuracy: 0.6195 Epoch 48/50 8108/8108 [==============================] - 47s 6ms/sample - loss: 0.0261 - accuracy: 0.9910 - val_loss: 6.0748 - val_accuracy: 0.4096 Epoch 49/50 8108/8108 [==============================] - 47s 6ms/sample - loss: 0.0508 - accuracy: 0.9848 - val_loss: 2.6370 - val_accuracy: 0.5634 Epoch 50/50 8108/8108 [==============================] - 47s 6ms/sample - loss: 0.0324 - accuracy: 0.9899 - val_loss: 3.5810 - val_accuracy: 0.4699 The AUC in the train set is 0.9203. The AUC in the validation set is 0.7205.
In [48]:
# Generate predictions in the form of probabilities for the validation set
valResNet50 = model.predict(shuffled_val_X, batch_size = 32)
# Generate the confusion matrix in the validation set
y_true = np.argmax(shuffled_val_y, axis=1)
y_predResNet50 = np.argmax(valResNet50, axis=1)
# Confusion matrix
pd.DataFrame(confusion_matrix(y_true, y_predResNet50), index=['True: Normal', 'True: Diffuse CM', 'True: PVNH'], columns=['Prediction: Normal', 'Prediction: Diffuse CM', 'Prediction: PVNH']).T
Out[48]:
| True: Normal | True: Diffuse CM | True: PVNH | |
|---|---|---|---|
| Prediction: Normal | 128 | 84 | 134 |
| Prediction: Diffuse CM | 20 | 58 | 1 |
| Prediction: PVNH | 11 | 5 | 40 |
In [49]:
# Calculate accuracy in the validation set
accuracy_ResNet50 = accuracy_score(y_true=y_true, y_pred=y_predResNet50)
print('The accuracy in the validation set is {:.4f}.'.format(accuracy_ResNet50))
The accuracy in the validation set is 0.4699.
In [50]:
# Calculate AUC in the validation set
auc_validResNet50 = roc_auc_score(shuffled_val_y, model.predict(shuffled_val_X))
print('The AUC in the validation set is {:.4f}.'.format(auc_validResNet50))
The AUC in the validation set is 0.7205.
In [51]:
# Classification report
print(classification_report(y_true, y_predResNet50, target_names=['Normal MRI', 'Diffuse CM', 'PVNH']))
precision recall f1-score support
Normal MRI 0.37 0.81 0.51 159
Diffuse CM 0.73 0.39 0.51 147
PVNH 0.71 0.23 0.35 175
accuracy 0.47 481
macro avg 0.61 0.48 0.46 481
weighted avg 0.61 0.47 0.45 481
Save model ResNet50¶
In [52]:
# Serialize model to JSON
model_json = model.to_json()
with open("ResNet50.json", "w") as json_file:
json_file.write(model_json)
# Serialize weights to HDF5
model.save_weights("ResNet50.h5")
Model visualization¶
In [53]:
# Visualize the structure and layers of the model
model.layers
Out[53]:
[<tensorflow.python.keras.engine.input_layer.InputLayer at 0x7f22340a18d0>, <tensorflow.python.keras.layers.convolutional.ZeroPadding2D at 0x7f22340a1828>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f22340a1e80>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f22343bdd68>, <tensorflow.python.keras.layers.core.Activation at 0x7f22343edd68>, <tensorflow.python.keras.layers.convolutional.ZeroPadding2D at 0x7f22343e9208>, <tensorflow.python.keras.layers.pooling.MaxPooling2D at 0x7f22343e9198>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f22343004e0>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f2234301e80>, <tensorflow.python.keras.layers.core.Activation at 0x7f312033bba8>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f312033be48>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f3120352e10>, <tensorflow.python.keras.layers.core.Activation at 0x7f3120361da0>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f223432dc50>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f31203610f0>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f2234301a20>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f220c51aa20>, <tensorflow.python.keras.layers.merge.Add at 0x7f220c51a9e8>, <tensorflow.python.keras.layers.core.Activation at 0x7f220c51ae48>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f220c51ae10>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f226c0c5d68>, <tensorflow.python.keras.layers.core.Activation at 0x7f226c0f0048>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f226c0d01d0>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f22344f0c88>, <tensorflow.python.keras.layers.core.Activation at 0x7f22344f0c50>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f22344f0940>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f22344f8e80>, <tensorflow.python.keras.layers.merge.Add at 0x7f3120265eb8>, <tensorflow.python.keras.layers.core.Activation at 0x7f31202650b8>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f3120265748>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f312026beb8>, <tensorflow.python.keras.layers.core.Activation at 0x7f312026be80>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f3120246588>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f22646b2668>, <tensorflow.python.keras.layers.core.Activation at 0x7f22646b2630>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f22646b29b0>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f312028dda0>, <tensorflow.python.keras.layers.merge.Add at 0x7f312028dd68>, <tensorflow.python.keras.layers.core.Activation at 0x7f31202ac080>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f31202bd860>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f31202c8390>, <tensorflow.python.keras.layers.core.Activation at 0x7f31202e8c18>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f31202e8ef0>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f226c092eb8>, <tensorflow.python.keras.layers.core.Activation at 0x7f226c09be48>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f31202ac128>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f226c09b048>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f31202bd898>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f2264589ac8>, <tensorflow.python.keras.layers.merge.Add at 0x7f2264589a90>, <tensorflow.python.keras.layers.core.Activation at 0x7f2264589ef0>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f2264589eb8>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f22645b3e10>, <tensorflow.python.keras.layers.core.Activation at 0x7f226403c588>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f226403c860>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f2264068d30>, <tensorflow.python.keras.layers.core.Activation at 0x7f2264068cf8>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f22640689e8>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f22345d3f28>, <tensorflow.python.keras.layers.merge.Add at 0x7f22345dcf60>, <tensorflow.python.keras.layers.core.Activation at 0x7f22345dc160>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f22345dc7f0>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f223458bf60>, <tensorflow.python.keras.layers.core.Activation at 0x7f223458bf28>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f2234592630>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f223453e710>, <tensorflow.python.keras.layers.core.Activation at 0x7f223453e6d8>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f223453ea58>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f223456be48>, <tensorflow.python.keras.layers.merge.Add at 0x7f223456be10>, <tensorflow.python.keras.layers.core.Activation at 0x7f2234572160>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f22345721d0>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f223451f940>, <tensorflow.python.keras.layers.core.Activation at 0x7f223451f908>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f223451fbe0>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f223448ccc0>, <tensorflow.python.keras.layers.core.Activation at 0x7f2234493ba8>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f2234493128>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f2234441828>, <tensorflow.python.keras.layers.merge.Add at 0x7f22344417f0>, <tensorflow.python.keras.layers.core.Activation at 0x7f2234441b70>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f2234474e10>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f2234474dd8>, <tensorflow.python.keras.layers.core.Activation at 0x7f22342e9198>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f22342e0588>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f223428ddd8>, <tensorflow.python.keras.layers.core.Activation at 0x7f223428dda0>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f2234441c18>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f22342970b8>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f223446cc50>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f223423f550>, <tensorflow.python.keras.layers.merge.Add at 0x7f223424a048>, <tensorflow.python.keras.layers.core.Activation at 0x7f223423f208>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f223423f898>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f223426fc18>, <tensorflow.python.keras.layers.core.Activation at 0x7f223426ffd0>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f2234275048>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f22342217b8>, <tensorflow.python.keras.layers.core.Activation at 0x7f2234221780>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f2234221b00>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f22341d0ef0>, <tensorflow.python.keras.layers.merge.Add at 0x7f22341d0eb8>, <tensorflow.python.keras.layers.core.Activation at 0x7f22341d6208>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f22341d6278>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f22341839e8>, <tensorflow.python.keras.layers.core.Activation at 0x7f22341839b0>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f2234183d30>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f22341aed68>, <tensorflow.python.keras.layers.core.Activation at 0x7f22341b8c50>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f22341b81d0>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f22341648d0>, <tensorflow.python.keras.layers.merge.Add at 0x7f2234164898>, <tensorflow.python.keras.layers.core.Activation at 0x7f2234164c18>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f2234164cc0>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f223410dcf8>, <tensorflow.python.keras.layers.core.Activation at 0x7f2234118eb8>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f2234118080>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f22340c5b38>, <tensorflow.python.keras.layers.core.Activation at 0x7f22340c5b00>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f22340c5e80>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f22340f0dd8>, <tensorflow.python.keras.layers.merge.Add at 0x7f22340f8d68>, <tensorflow.python.keras.layers.core.Activation at 0x7f22340f80b8>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f22340f85f8>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f220c7a5d68>, <tensorflow.python.keras.layers.core.Activation at 0x7f220c7a5d30>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f220c7b0438>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f220c6d9518>, <tensorflow.python.keras.layers.core.Activation at 0x7f220c6e2048>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f220c6d91d0>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f220c605c50>, <tensorflow.python.keras.layers.merge.Add at 0x7f220c605c18>, <tensorflow.python.keras.layers.core.Activation at 0x7f220c605ef0>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f220c60e780>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f220c3fa748>, <tensorflow.python.keras.layers.core.Activation at 0x7f220c3fa710>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f220c3fa9e8>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f220c426eb8>, <tensorflow.python.keras.layers.core.Activation at 0x7f220c426e80>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f220c42f1d0>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f220c3db630>, <tensorflow.python.keras.layers.merge.Add at 0x7f220c3db5f8>, <tensorflow.python.keras.layers.core.Activation at 0x7f220c3db978>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f220c38fc18>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f220c38fbe0>, <tensorflow.python.keras.layers.core.Activation at 0x7f220c33bf60>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f220c33b390>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f220c366be0>, <tensorflow.python.keras.layers.core.Activation at 0x7f220c366ba8>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f220c3dba20>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f220c366e48>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f220c387cf8>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f220c312e80>, <tensorflow.python.keras.layers.merge.Add at 0x7f220c31be10>, <tensorflow.python.keras.layers.core.Activation at 0x7f220c31b160>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f220c31b6a0>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f220c2c6e10>, <tensorflow.python.keras.layers.core.Activation at 0x7f220c2c6dd8>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f220c2ce4e0>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f220c23d5c0>, <tensorflow.python.keras.layers.core.Activation at 0x7f220c23d588>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f220c23d908>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f220c268cf8>, <tensorflow.python.keras.layers.merge.Add at 0x7f220c268cc0>, <tensorflow.python.keras.layers.core.Activation at 0x7f220c2689b0>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f220c270080>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f220c21d7f0>, <tensorflow.python.keras.layers.core.Activation at 0x7f220c21d7b8>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f220c21da90>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f220c1c8f60>, <tensorflow.python.keras.layers.core.Activation at 0x7f220c1c8f28>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f220c1d3278>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f220c17d6d8>, <tensorflow.python.keras.layers.merge.Add at 0x7f220c17d6a0>, <tensorflow.python.keras.layers.core.Activation at 0x7f220c17da20>, <tensorflow.python.keras.layers.pooling.GlobalAveragePooling2D at 0x7f220c1b3cf8>, <tensorflow.python.keras.layers.core.Dense at 0x7f22340a19b0>, <tensorflow.python.keras.layers.core.Dropout at 0x7f220c1b3da0>, <tensorflow.python.keras.layers.core.Dense at 0x7f220c084cc0>, <tensorflow.python.keras.layers.core.Dropout at 0x7f220c084e80>, <tensorflow.python.keras.layers.core.Dense at 0x7f220c0f4588>, <tensorflow.python.keras.layers.core.Dense at 0x7f220c090be0>]
In [54]:
# Visualize the structure and layers of the model
print(model.summary())
Model: "model_122"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_3 (InputLayer) [(None, 512, 512, 3) 0
__________________________________________________________________________________________________
conv1_pad (ZeroPadding2D) (None, 518, 518, 3) 0 input_3[0][0]
__________________________________________________________________________________________________
conv1_conv (Conv2D) (None, 256, 256, 64) 9472 conv1_pad[0][0]
__________________________________________________________________________________________________
conv1_bn (BatchNormalization) (None, 256, 256, 64) 256 conv1_conv[0][0]
__________________________________________________________________________________________________
conv1_relu (Activation) (None, 256, 256, 64) 0 conv1_bn[0][0]
__________________________________________________________________________________________________
pool1_pad (ZeroPadding2D) (None, 258, 258, 64) 0 conv1_relu[0][0]
__________________________________________________________________________________________________
pool1_pool (MaxPooling2D) (None, 128, 128, 64) 0 pool1_pad[0][0]
__________________________________________________________________________________________________
conv2_block1_1_conv (Conv2D) (None, 128, 128, 64) 4160 pool1_pool[0][0]
__________________________________________________________________________________________________
conv2_block1_1_bn (BatchNormali (None, 128, 128, 64) 256 conv2_block1_1_conv[0][0]
__________________________________________________________________________________________________
conv2_block1_1_relu (Activation (None, 128, 128, 64) 0 conv2_block1_1_bn[0][0]
__________________________________________________________________________________________________
conv2_block1_2_conv (Conv2D) (None, 128, 128, 64) 36928 conv2_block1_1_relu[0][0]
__________________________________________________________________________________________________
conv2_block1_2_bn (BatchNormali (None, 128, 128, 64) 256 conv2_block1_2_conv[0][0]
__________________________________________________________________________________________________
conv2_block1_2_relu (Activation (None, 128, 128, 64) 0 conv2_block1_2_bn[0][0]
__________________________________________________________________________________________________
conv2_block1_0_conv (Conv2D) (None, 128, 128, 256 16640 pool1_pool[0][0]
__________________________________________________________________________________________________
conv2_block1_3_conv (Conv2D) (None, 128, 128, 256 16640 conv2_block1_2_relu[0][0]
__________________________________________________________________________________________________
conv2_block1_0_bn (BatchNormali (None, 128, 128, 256 1024 conv2_block1_0_conv[0][0]
__________________________________________________________________________________________________
conv2_block1_3_bn (BatchNormali (None, 128, 128, 256 1024 conv2_block1_3_conv[0][0]
__________________________________________________________________________________________________
conv2_block1_add (Add) (None, 128, 128, 256 0 conv2_block1_0_bn[0][0]
conv2_block1_3_bn[0][0]
__________________________________________________________________________________________________
conv2_block1_out (Activation) (None, 128, 128, 256 0 conv2_block1_add[0][0]
__________________________________________________________________________________________________
conv2_block2_1_conv (Conv2D) (None, 128, 128, 64) 16448 conv2_block1_out[0][0]
__________________________________________________________________________________________________
conv2_block2_1_bn (BatchNormali (None, 128, 128, 64) 256 conv2_block2_1_conv[0][0]
__________________________________________________________________________________________________
conv2_block2_1_relu (Activation (None, 128, 128, 64) 0 conv2_block2_1_bn[0][0]
__________________________________________________________________________________________________
conv2_block2_2_conv (Conv2D) (None, 128, 128, 64) 36928 conv2_block2_1_relu[0][0]
__________________________________________________________________________________________________
conv2_block2_2_bn (BatchNormali (None, 128, 128, 64) 256 conv2_block2_2_conv[0][0]
__________________________________________________________________________________________________
conv2_block2_2_relu (Activation (None, 128, 128, 64) 0 conv2_block2_2_bn[0][0]
__________________________________________________________________________________________________
conv2_block2_3_conv (Conv2D) (None, 128, 128, 256 16640 conv2_block2_2_relu[0][0]
__________________________________________________________________________________________________
conv2_block2_3_bn (BatchNormali (None, 128, 128, 256 1024 conv2_block2_3_conv[0][0]
__________________________________________________________________________________________________
conv2_block2_add (Add) (None, 128, 128, 256 0 conv2_block1_out[0][0]
conv2_block2_3_bn[0][0]
__________________________________________________________________________________________________
conv2_block2_out (Activation) (None, 128, 128, 256 0 conv2_block2_add[0][0]
__________________________________________________________________________________________________
conv2_block3_1_conv (Conv2D) (None, 128, 128, 64) 16448 conv2_block2_out[0][0]
__________________________________________________________________________________________________
conv2_block3_1_bn (BatchNormali (None, 128, 128, 64) 256 conv2_block3_1_conv[0][0]
__________________________________________________________________________________________________
conv2_block3_1_relu (Activation (None, 128, 128, 64) 0 conv2_block3_1_bn[0][0]
__________________________________________________________________________________________________
conv2_block3_2_conv (Conv2D) (None, 128, 128, 64) 36928 conv2_block3_1_relu[0][0]
__________________________________________________________________________________________________
conv2_block3_2_bn (BatchNormali (None, 128, 128, 64) 256 conv2_block3_2_conv[0][0]
__________________________________________________________________________________________________
conv2_block3_2_relu (Activation (None, 128, 128, 64) 0 conv2_block3_2_bn[0][0]
__________________________________________________________________________________________________
conv2_block3_3_conv (Conv2D) (None, 128, 128, 256 16640 conv2_block3_2_relu[0][0]
__________________________________________________________________________________________________
conv2_block3_3_bn (BatchNormali (None, 128, 128, 256 1024 conv2_block3_3_conv[0][0]
__________________________________________________________________________________________________
conv2_block3_add (Add) (None, 128, 128, 256 0 conv2_block2_out[0][0]
conv2_block3_3_bn[0][0]
__________________________________________________________________________________________________
conv2_block3_out (Activation) (None, 128, 128, 256 0 conv2_block3_add[0][0]
__________________________________________________________________________________________________
conv3_block1_1_conv (Conv2D) (None, 64, 64, 128) 32896 conv2_block3_out[0][0]
__________________________________________________________________________________________________
conv3_block1_1_bn (BatchNormali (None, 64, 64, 128) 512 conv3_block1_1_conv[0][0]
__________________________________________________________________________________________________
conv3_block1_1_relu (Activation (None, 64, 64, 128) 0 conv3_block1_1_bn[0][0]
__________________________________________________________________________________________________
conv3_block1_2_conv (Conv2D) (None, 64, 64, 128) 147584 conv3_block1_1_relu[0][0]
__________________________________________________________________________________________________
conv3_block1_2_bn (BatchNormali (None, 64, 64, 128) 512 conv3_block1_2_conv[0][0]
__________________________________________________________________________________________________
conv3_block1_2_relu (Activation (None, 64, 64, 128) 0 conv3_block1_2_bn[0][0]
__________________________________________________________________________________________________
conv3_block1_0_conv (Conv2D) (None, 64, 64, 512) 131584 conv2_block3_out[0][0]
__________________________________________________________________________________________________
conv3_block1_3_conv (Conv2D) (None, 64, 64, 512) 66048 conv3_block1_2_relu[0][0]
__________________________________________________________________________________________________
conv3_block1_0_bn (BatchNormali (None, 64, 64, 512) 2048 conv3_block1_0_conv[0][0]
__________________________________________________________________________________________________
conv3_block1_3_bn (BatchNormali (None, 64, 64, 512) 2048 conv3_block1_3_conv[0][0]
__________________________________________________________________________________________________
conv3_block1_add (Add) (None, 64, 64, 512) 0 conv3_block1_0_bn[0][0]
conv3_block1_3_bn[0][0]
__________________________________________________________________________________________________
conv3_block1_out (Activation) (None, 64, 64, 512) 0 conv3_block1_add[0][0]
__________________________________________________________________________________________________
conv3_block2_1_conv (Conv2D) (None, 64, 64, 128) 65664 conv3_block1_out[0][0]
__________________________________________________________________________________________________
conv3_block2_1_bn (BatchNormali (None, 64, 64, 128) 512 conv3_block2_1_conv[0][0]
__________________________________________________________________________________________________
conv3_block2_1_relu (Activation (None, 64, 64, 128) 0 conv3_block2_1_bn[0][0]
__________________________________________________________________________________________________
conv3_block2_2_conv (Conv2D) (None, 64, 64, 128) 147584 conv3_block2_1_relu[0][0]
__________________________________________________________________________________________________
conv3_block2_2_bn (BatchNormali (None, 64, 64, 128) 512 conv3_block2_2_conv[0][0]
__________________________________________________________________________________________________
conv3_block2_2_relu (Activation (None, 64, 64, 128) 0 conv3_block2_2_bn[0][0]
__________________________________________________________________________________________________
conv3_block2_3_conv (Conv2D) (None, 64, 64, 512) 66048 conv3_block2_2_relu[0][0]
__________________________________________________________________________________________________
conv3_block2_3_bn (BatchNormali (None, 64, 64, 512) 2048 conv3_block2_3_conv[0][0]
__________________________________________________________________________________________________
conv3_block2_add (Add) (None, 64, 64, 512) 0 conv3_block1_out[0][0]
conv3_block2_3_bn[0][0]
__________________________________________________________________________________________________
conv3_block2_out (Activation) (None, 64, 64, 512) 0 conv3_block2_add[0][0]
__________________________________________________________________________________________________
conv3_block3_1_conv (Conv2D) (None, 64, 64, 128) 65664 conv3_block2_out[0][0]
__________________________________________________________________________________________________
conv3_block3_1_bn (BatchNormali (None, 64, 64, 128) 512 conv3_block3_1_conv[0][0]
__________________________________________________________________________________________________
conv3_block3_1_relu (Activation (None, 64, 64, 128) 0 conv3_block3_1_bn[0][0]
__________________________________________________________________________________________________
conv3_block3_2_conv (Conv2D) (None, 64, 64, 128) 147584 conv3_block3_1_relu[0][0]
__________________________________________________________________________________________________
conv3_block3_2_bn (BatchNormali (None, 64, 64, 128) 512 conv3_block3_2_conv[0][0]
__________________________________________________________________________________________________
conv3_block3_2_relu (Activation (None, 64, 64, 128) 0 conv3_block3_2_bn[0][0]
__________________________________________________________________________________________________
conv3_block3_3_conv (Conv2D) (None, 64, 64, 512) 66048 conv3_block3_2_relu[0][0]
__________________________________________________________________________________________________
conv3_block3_3_bn (BatchNormali (None, 64, 64, 512) 2048 conv3_block3_3_conv[0][0]
__________________________________________________________________________________________________
conv3_block3_add (Add) (None, 64, 64, 512) 0 conv3_block2_out[0][0]
conv3_block3_3_bn[0][0]
__________________________________________________________________________________________________
conv3_block3_out (Activation) (None, 64, 64, 512) 0 conv3_block3_add[0][0]
__________________________________________________________________________________________________
conv3_block4_1_conv (Conv2D) (None, 64, 64, 128) 65664 conv3_block3_out[0][0]
__________________________________________________________________________________________________
conv3_block4_1_bn (BatchNormali (None, 64, 64, 128) 512 conv3_block4_1_conv[0][0]
__________________________________________________________________________________________________
conv3_block4_1_relu (Activation (None, 64, 64, 128) 0 conv3_block4_1_bn[0][0]
__________________________________________________________________________________________________
conv3_block4_2_conv (Conv2D) (None, 64, 64, 128) 147584 conv3_block4_1_relu[0][0]
__________________________________________________________________________________________________
conv3_block4_2_bn (BatchNormali (None, 64, 64, 128) 512 conv3_block4_2_conv[0][0]
__________________________________________________________________________________________________
conv3_block4_2_relu (Activation (None, 64, 64, 128) 0 conv3_block4_2_bn[0][0]
__________________________________________________________________________________________________
conv3_block4_3_conv (Conv2D) (None, 64, 64, 512) 66048 conv3_block4_2_relu[0][0]
__________________________________________________________________________________________________
conv3_block4_3_bn (BatchNormali (None, 64, 64, 512) 2048 conv3_block4_3_conv[0][0]
__________________________________________________________________________________________________
conv3_block4_add (Add) (None, 64, 64, 512) 0 conv3_block3_out[0][0]
conv3_block4_3_bn[0][0]
__________________________________________________________________________________________________
conv3_block4_out (Activation) (None, 64, 64, 512) 0 conv3_block4_add[0][0]
__________________________________________________________________________________________________
conv4_block1_1_conv (Conv2D) (None, 32, 32, 256) 131328 conv3_block4_out[0][0]
__________________________________________________________________________________________________
conv4_block1_1_bn (BatchNormali (None, 32, 32, 256) 1024 conv4_block1_1_conv[0][0]
__________________________________________________________________________________________________
conv4_block1_1_relu (Activation (None, 32, 32, 256) 0 conv4_block1_1_bn[0][0]
__________________________________________________________________________________________________
conv4_block1_2_conv (Conv2D) (None, 32, 32, 256) 590080 conv4_block1_1_relu[0][0]
__________________________________________________________________________________________________
conv4_block1_2_bn (BatchNormali (None, 32, 32, 256) 1024 conv4_block1_2_conv[0][0]
__________________________________________________________________________________________________
conv4_block1_2_relu (Activation (None, 32, 32, 256) 0 conv4_block1_2_bn[0][0]
__________________________________________________________________________________________________
conv4_block1_0_conv (Conv2D) (None, 32, 32, 1024) 525312 conv3_block4_out[0][0]
__________________________________________________________________________________________________
conv4_block1_3_conv (Conv2D) (None, 32, 32, 1024) 263168 conv4_block1_2_relu[0][0]
__________________________________________________________________________________________________
conv4_block1_0_bn (BatchNormali (None, 32, 32, 1024) 4096 conv4_block1_0_conv[0][0]
__________________________________________________________________________________________________
conv4_block1_3_bn (BatchNormali (None, 32, 32, 1024) 4096 conv4_block1_3_conv[0][0]
__________________________________________________________________________________________________
conv4_block1_add (Add) (None, 32, 32, 1024) 0 conv4_block1_0_bn[0][0]
conv4_block1_3_bn[0][0]
__________________________________________________________________________________________________
conv4_block1_out (Activation) (None, 32, 32, 1024) 0 conv4_block1_add[0][0]
__________________________________________________________________________________________________
conv4_block2_1_conv (Conv2D) (None, 32, 32, 256) 262400 conv4_block1_out[0][0]
__________________________________________________________________________________________________
conv4_block2_1_bn (BatchNormali (None, 32, 32, 256) 1024 conv4_block2_1_conv[0][0]
__________________________________________________________________________________________________
conv4_block2_1_relu (Activation (None, 32, 32, 256) 0 conv4_block2_1_bn[0][0]
__________________________________________________________________________________________________
conv4_block2_2_conv (Conv2D) (None, 32, 32, 256) 590080 conv4_block2_1_relu[0][0]
__________________________________________________________________________________________________
conv4_block2_2_bn (BatchNormali (None, 32, 32, 256) 1024 conv4_block2_2_conv[0][0]
__________________________________________________________________________________________________
conv4_block2_2_relu (Activation (None, 32, 32, 256) 0 conv4_block2_2_bn[0][0]
__________________________________________________________________________________________________
conv4_block2_3_conv (Conv2D) (None, 32, 32, 1024) 263168 conv4_block2_2_relu[0][0]
__________________________________________________________________________________________________
conv4_block2_3_bn (BatchNormali (None, 32, 32, 1024) 4096 conv4_block2_3_conv[0][0]
__________________________________________________________________________________________________
conv4_block2_add (Add) (None, 32, 32, 1024) 0 conv4_block1_out[0][0]
conv4_block2_3_bn[0][0]
__________________________________________________________________________________________________
conv4_block2_out (Activation) (None, 32, 32, 1024) 0 conv4_block2_add[0][0]
__________________________________________________________________________________________________
conv4_block3_1_conv (Conv2D) (None, 32, 32, 256) 262400 conv4_block2_out[0][0]
__________________________________________________________________________________________________
conv4_block3_1_bn (BatchNormali (None, 32, 32, 256) 1024 conv4_block3_1_conv[0][0]
__________________________________________________________________________________________________
conv4_block3_1_relu (Activation (None, 32, 32, 256) 0 conv4_block3_1_bn[0][0]
__________________________________________________________________________________________________
conv4_block3_2_conv (Conv2D) (None, 32, 32, 256) 590080 conv4_block3_1_relu[0][0]
__________________________________________________________________________________________________
conv4_block3_2_bn (BatchNormali (None, 32, 32, 256) 1024 conv4_block3_2_conv[0][0]
__________________________________________________________________________________________________
conv4_block3_2_relu (Activation (None, 32, 32, 256) 0 conv4_block3_2_bn[0][0]
__________________________________________________________________________________________________
conv4_block3_3_conv (Conv2D) (None, 32, 32, 1024) 263168 conv4_block3_2_relu[0][0]
__________________________________________________________________________________________________
conv4_block3_3_bn (BatchNormali (None, 32, 32, 1024) 4096 conv4_block3_3_conv[0][0]
__________________________________________________________________________________________________
conv4_block3_add (Add) (None, 32, 32, 1024) 0 conv4_block2_out[0][0]
conv4_block3_3_bn[0][0]
__________________________________________________________________________________________________
conv4_block3_out (Activation) (None, 32, 32, 1024) 0 conv4_block3_add[0][0]
__________________________________________________________________________________________________
conv4_block4_1_conv (Conv2D) (None, 32, 32, 256) 262400 conv4_block3_out[0][0]
__________________________________________________________________________________________________
conv4_block4_1_bn (BatchNormali (None, 32, 32, 256) 1024 conv4_block4_1_conv[0][0]
__________________________________________________________________________________________________
conv4_block4_1_relu (Activation (None, 32, 32, 256) 0 conv4_block4_1_bn[0][0]
__________________________________________________________________________________________________
conv4_block4_2_conv (Conv2D) (None, 32, 32, 256) 590080 conv4_block4_1_relu[0][0]
__________________________________________________________________________________________________
conv4_block4_2_bn (BatchNormali (None, 32, 32, 256) 1024 conv4_block4_2_conv[0][0]
__________________________________________________________________________________________________
conv4_block4_2_relu (Activation (None, 32, 32, 256) 0 conv4_block4_2_bn[0][0]
__________________________________________________________________________________________________
conv4_block4_3_conv (Conv2D) (None, 32, 32, 1024) 263168 conv4_block4_2_relu[0][0]
__________________________________________________________________________________________________
conv4_block4_3_bn (BatchNormali (None, 32, 32, 1024) 4096 conv4_block4_3_conv[0][0]
__________________________________________________________________________________________________
conv4_block4_add (Add) (None, 32, 32, 1024) 0 conv4_block3_out[0][0]
conv4_block4_3_bn[0][0]
__________________________________________________________________________________________________
conv4_block4_out (Activation) (None, 32, 32, 1024) 0 conv4_block4_add[0][0]
__________________________________________________________________________________________________
conv4_block5_1_conv (Conv2D) (None, 32, 32, 256) 262400 conv4_block4_out[0][0]
__________________________________________________________________________________________________
conv4_block5_1_bn (BatchNormali (None, 32, 32, 256) 1024 conv4_block5_1_conv[0][0]
__________________________________________________________________________________________________
conv4_block5_1_relu (Activation (None, 32, 32, 256) 0 conv4_block5_1_bn[0][0]
__________________________________________________________________________________________________
conv4_block5_2_conv (Conv2D) (None, 32, 32, 256) 590080 conv4_block5_1_relu[0][0]
__________________________________________________________________________________________________
conv4_block5_2_bn (BatchNormali (None, 32, 32, 256) 1024 conv4_block5_2_conv[0][0]
__________________________________________________________________________________________________
conv4_block5_2_relu (Activation (None, 32, 32, 256) 0 conv4_block5_2_bn[0][0]
__________________________________________________________________________________________________
conv4_block5_3_conv (Conv2D) (None, 32, 32, 1024) 263168 conv4_block5_2_relu[0][0]
__________________________________________________________________________________________________
conv4_block5_3_bn (BatchNormali (None, 32, 32, 1024) 4096 conv4_block5_3_conv[0][0]
__________________________________________________________________________________________________
conv4_block5_add (Add) (None, 32, 32, 1024) 0 conv4_block4_out[0][0]
conv4_block5_3_bn[0][0]
__________________________________________________________________________________________________
conv4_block5_out (Activation) (None, 32, 32, 1024) 0 conv4_block5_add[0][0]
__________________________________________________________________________________________________
conv4_block6_1_conv (Conv2D) (None, 32, 32, 256) 262400 conv4_block5_out[0][0]
__________________________________________________________________________________________________
conv4_block6_1_bn (BatchNormali (None, 32, 32, 256) 1024 conv4_block6_1_conv[0][0]
__________________________________________________________________________________________________
conv4_block6_1_relu (Activation (None, 32, 32, 256) 0 conv4_block6_1_bn[0][0]
__________________________________________________________________________________________________
conv4_block6_2_conv (Conv2D) (None, 32, 32, 256) 590080 conv4_block6_1_relu[0][0]
__________________________________________________________________________________________________
conv4_block6_2_bn (BatchNormali (None, 32, 32, 256) 1024 conv4_block6_2_conv[0][0]
__________________________________________________________________________________________________
conv4_block6_2_relu (Activation (None, 32, 32, 256) 0 conv4_block6_2_bn[0][0]
__________________________________________________________________________________________________
conv4_block6_3_conv (Conv2D) (None, 32, 32, 1024) 263168 conv4_block6_2_relu[0][0]
__________________________________________________________________________________________________
conv4_block6_3_bn (BatchNormali (None, 32, 32, 1024) 4096 conv4_block6_3_conv[0][0]
__________________________________________________________________________________________________
conv4_block6_add (Add) (None, 32, 32, 1024) 0 conv4_block5_out[0][0]
conv4_block6_3_bn[0][0]
__________________________________________________________________________________________________
conv4_block6_out (Activation) (None, 32, 32, 1024) 0 conv4_block6_add[0][0]
__________________________________________________________________________________________________
conv5_block1_1_conv (Conv2D) (None, 16, 16, 512) 524800 conv4_block6_out[0][0]
__________________________________________________________________________________________________
conv5_block1_1_bn (BatchNormali (None, 16, 16, 512) 2048 conv5_block1_1_conv[0][0]
__________________________________________________________________________________________________
conv5_block1_1_relu (Activation (None, 16, 16, 512) 0 conv5_block1_1_bn[0][0]
__________________________________________________________________________________________________
conv5_block1_2_conv (Conv2D) (None, 16, 16, 512) 2359808 conv5_block1_1_relu[0][0]
__________________________________________________________________________________________________
conv5_block1_2_bn (BatchNormali (None, 16, 16, 512) 2048 conv5_block1_2_conv[0][0]
__________________________________________________________________________________________________
conv5_block1_2_relu (Activation (None, 16, 16, 512) 0 conv5_block1_2_bn[0][0]
__________________________________________________________________________________________________
conv5_block1_0_conv (Conv2D) (None, 16, 16, 2048) 2099200 conv4_block6_out[0][0]
__________________________________________________________________________________________________
conv5_block1_3_conv (Conv2D) (None, 16, 16, 2048) 1050624 conv5_block1_2_relu[0][0]
__________________________________________________________________________________________________
conv5_block1_0_bn (BatchNormali (None, 16, 16, 2048) 8192 conv5_block1_0_conv[0][0]
__________________________________________________________________________________________________
conv5_block1_3_bn (BatchNormali (None, 16, 16, 2048) 8192 conv5_block1_3_conv[0][0]
__________________________________________________________________________________________________
conv5_block1_add (Add) (None, 16, 16, 2048) 0 conv5_block1_0_bn[0][0]
conv5_block1_3_bn[0][0]
__________________________________________________________________________________________________
conv5_block1_out (Activation) (None, 16, 16, 2048) 0 conv5_block1_add[0][0]
__________________________________________________________________________________________________
conv5_block2_1_conv (Conv2D) (None, 16, 16, 512) 1049088 conv5_block1_out[0][0]
__________________________________________________________________________________________________
conv5_block2_1_bn (BatchNormali (None, 16, 16, 512) 2048 conv5_block2_1_conv[0][0]
__________________________________________________________________________________________________
conv5_block2_1_relu (Activation (None, 16, 16, 512) 0 conv5_block2_1_bn[0][0]
__________________________________________________________________________________________________
conv5_block2_2_conv (Conv2D) (None, 16, 16, 512) 2359808 conv5_block2_1_relu[0][0]
__________________________________________________________________________________________________
conv5_block2_2_bn (BatchNormali (None, 16, 16, 512) 2048 conv5_block2_2_conv[0][0]
__________________________________________________________________________________________________
conv5_block2_2_relu (Activation (None, 16, 16, 512) 0 conv5_block2_2_bn[0][0]
__________________________________________________________________________________________________
conv5_block2_3_conv (Conv2D) (None, 16, 16, 2048) 1050624 conv5_block2_2_relu[0][0]
__________________________________________________________________________________________________
conv5_block2_3_bn (BatchNormali (None, 16, 16, 2048) 8192 conv5_block2_3_conv[0][0]
__________________________________________________________________________________________________
conv5_block2_add (Add) (None, 16, 16, 2048) 0 conv5_block1_out[0][0]
conv5_block2_3_bn[0][0]
__________________________________________________________________________________________________
conv5_block2_out (Activation) (None, 16, 16, 2048) 0 conv5_block2_add[0][0]
__________________________________________________________________________________________________
conv5_block3_1_conv (Conv2D) (None, 16, 16, 512) 1049088 conv5_block2_out[0][0]
__________________________________________________________________________________________________
conv5_block3_1_bn (BatchNormali (None, 16, 16, 512) 2048 conv5_block3_1_conv[0][0]
__________________________________________________________________________________________________
conv5_block3_1_relu (Activation (None, 16, 16, 512) 0 conv5_block3_1_bn[0][0]
__________________________________________________________________________________________________
conv5_block3_2_conv (Conv2D) (None, 16, 16, 512) 2359808 conv5_block3_1_relu[0][0]
__________________________________________________________________________________________________
conv5_block3_2_bn (BatchNormali (None, 16, 16, 512) 2048 conv5_block3_2_conv[0][0]
__________________________________________________________________________________________________
conv5_block3_2_relu (Activation (None, 16, 16, 512) 0 conv5_block3_2_bn[0][0]
__________________________________________________________________________________________________
conv5_block3_3_conv (Conv2D) (None, 16, 16, 2048) 1050624 conv5_block3_2_relu[0][0]
__________________________________________________________________________________________________
conv5_block3_3_bn (BatchNormali (None, 16, 16, 2048) 8192 conv5_block3_3_conv[0][0]
__________________________________________________________________________________________________
conv5_block3_add (Add) (None, 16, 16, 2048) 0 conv5_block2_out[0][0]
conv5_block3_3_bn[0][0]
__________________________________________________________________________________________________
conv5_block3_out (Activation) (None, 16, 16, 2048) 0 conv5_block3_add[0][0]
__________________________________________________________________________________________________
global_average_pooling2d_2 (Glo (None, 2048) 0 conv5_block3_out[0][0]
__________________________________________________________________________________________________
dense_8 (Dense) (None, 516) 1057284 global_average_pooling2d_2[0][0]
__________________________________________________________________________________________________
dropout_4 (Dropout) (None, 516) 0 dense_8[0][0]
__________________________________________________________________________________________________
dense_9 (Dense) (None, 256) 132352 dropout_4[0][0]
__________________________________________________________________________________________________
dropout_5 (Dropout) (None, 256) 0 dense_9[0][0]
__________________________________________________________________________________________________
dense_10 (Dense) (None, 64) 16448 dropout_5[0][0]
__________________________________________________________________________________________________
dense_11 (Dense) (None, 3) 195 dense_10[0][0]
==================================================================================================
Total params: 24,793,991
Trainable params: 10,137,607
Non-trainable params: 14,656,384
__________________________________________________________________________________________________
None
In [55]:
# Define modifier to replace the sigmoid function of the last layer to a linear function
def model_modifier(m):
m.layers[-1].activation = tf.keras.activations.linear
# Define losses functions. 0 is the index for a normal MRI
loss_normal = lambda output: K.mean(output[:, 0])
# Define losses functions. 1 is the index for a diffuse malformation of cortical development MRI
loss_diffuseMCD = lambda output: K.mean(output[:, 1])
# Define losses functions. 2 is the index for a PVNH MRI
loss_PVNH = lambda output: K.mean(output[:, 2])
# Create Gradcam object
gradcam = Gradcam(model, model_modifier)
# Create Saliency object
saliency = Saliency(model, model_modifier)
# Iterate through the MRIs in test set
# Set background to white color
plt.rcParams['axes.facecolor']='white'
plt.rcParams['figure.facecolor']='white'
plt.rcParams['figure.edgecolor']='white'
print('\n \n' + '\033[1m' + 'EACH ORIGINAL MRI IS ANALYZED WITH TWO METHODS: CLASS ACTIVATION MAP (UPPER ROW) AND SALIENCY MAP (LOWER ROW)' + '\033[0m' + '\n')
print('\033[1m' + 'EACH MAP IS SUPERIMPOSED ON THE ORIGINAL MRI WITH A TRANSPARENCY THAT IS INVERSELY PROPORTIONAL TO THE ESTIMATED PROBABILITY OF THE MRI BELONGING TO THAT CATEGORY (NORMAL MRI, DIFFUSE CORTICAL MALFORMATION, OR PERIVENTRICULAR NODULAR HETEROTOPIA) \n \nHIGHER ESTIMATED PROBABILITIES PRODUCE CLEARLY SEEN MAPS OVERLAID ON THE ORIGINAL MRI AND LOWER ESTIMATED PROBABILITIES PRODUCE VERY TRANSPARENT OR NOT APPRECIABLE MAPS OVERLAID ON THE ORIGINAL MRI' + '\033[0m'+ '\n')
for i in range(20):
# Print spaces to separate from the next image
print('\n \n \n \n \n \n')
# Print real classification of the image
if y_true[i]==0:
real_classification='Normal MRI'
elif y_true[i]==1:
real_classification='Diffuse MCD'
else:
real_classification='PVNH'
print('\033[1m' + 'REAL CLASSIFICATION OF THE IMAGE: {}'.format(real_classification) + '\033[0m')
# Print model classification and model probability of MCD
if y_predResNet50[i]==0:
predicted_classification='Normal MRI'
elif y_predResNet50[i]==1:
predicted_classification='Diffuse MCD'
else:
predicted_classification='PVNH'
print('\033[1m' + 'MODEL CLASSIFICATION OF THE IMAGE: {}'.format(predicted_classification) + '\033[0m \n')
print('\033[1m' + ' Prob. Normal MRI: {:.4f} '.format(valResNet50[i][0]) + 'Prob. Diffuse MCD: {:.4f} '.format(valResNet50[i][1]), 'Prob. PVNH: {:.4f}'.format(valResNet50[i][2]) + '\033[0m')
# Arrays to plot
original_image=shuffled_val_X[i]
list_heatmaps=[
# GradCam heatmap for normal MRI
normalize(gradcam(loss_normal, shuffled_val_X[i])),
# GradCam heatmap for diffuse MCD
normalize(gradcam(loss_diffuseMCD, shuffled_val_X[i])),
# GradCam heatmap for PVNH
normalize(gradcam(loss_PVNH, shuffled_val_X[i])),
# Saliency heatmap for normal MRI
normalize(saliency(loss_normal, seed_input=np.expand_dims(shuffled_val_X[i], axis=0), smooth_noise=0.2)),
# Saliency heatmap for diffuse MCD
normalize(saliency(loss_diffuseMCD, seed_input=np.expand_dims(shuffled_val_X[i], axis=0), smooth_noise=0.2)),
# Saliency heatmap for PVNH
normalize(saliency(loss_PVNH, seed_input=np.expand_dims(shuffled_val_X[i], axis=0), smooth_noise=0.2))
]
# Define figure
f=plt.figure(figsize=(20, 8))
# Define the image grid
grid = ImageGrid(f, 111,
nrows_ncols=(2, 3),
axes_pad=0.05,
share_all=True,
cbar_location="right",
cbar_mode=None,
cbar_size="2%",
cbar_pad=0.15)
# Iterate over the graphs
for j, axis in enumerate(grid):
# Plot original
im=axis.imshow(original_image)
im=axis.imshow(list_heatmaps[j][0], cmap='jet', alpha=0.5*valResNet50[i][j%3])
im=axis.set_xticks([])
im=axis.set_yticks([])
# Create scalarmappable for obtaining the colorbar from 0 to 1
sm = plt.cm.ScalarMappable(cmap='jet', norm=plt.Normalize(vmin=0, vmax=1))
plt.colorbar(sm)
plt.show()
EACH ORIGINAL MRI IS ANALYZED WITH TWO METHODS: CLASS ACTIVATION MAP (UPPER ROW) AND SALIENCY MAP (LOWER ROW) EACH MAP IS SUPERIMPOSED ON THE ORIGINAL MRI WITH A TRANSPARENCY THAT IS INVERSELY PROPORTIONAL TO THE ESTIMATED PROBABILITY OF THE MRI BELONGING TO THAT CATEGORY (NORMAL MRI, DIFFUSE CORTICAL MALFORMATION, OR PERIVENTRICULAR NODULAR HETEROTOPIA) HIGHER ESTIMATED PROBABILITIES PRODUCE CLEARLY SEEN MAPS OVERLAID ON THE ORIGINAL MRI AND LOWER ESTIMATED PROBABILITIES PRODUCE VERY TRANSPARENT OR NOT APPRECIABLE MAPS OVERLAID ON THE ORIGINAL MRI REAL CLASSIFICATION OF THE IMAGE: PVNH MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 0.6853 Prob. Diffuse MCD: 0.0047 Prob. PVNH: 0.3099
REAL CLASSIFICATION OF THE IMAGE: PVNH MODEL CLASSIFICATION OF THE IMAGE: PVNH Prob. Normal MRI: 0.4504 Prob. Diffuse MCD: 0.0016 Prob. PVNH: 0.5480
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 0.6581 Prob. Diffuse MCD: 0.3403 Prob. PVNH: 0.0015
REAL CLASSIFICATION OF THE IMAGE: PVNH MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 0.9922 Prob. Diffuse MCD: 0.0029 Prob. PVNH: 0.0050
REAL CLASSIFICATION OF THE IMAGE: PVNH MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 0.9530 Prob. Diffuse MCD: 0.0024 Prob. PVNH: 0.0446
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 0.9967 Prob. Diffuse MCD: 0.0033 Prob. PVNH: 0.0000
REAL CLASSIFICATION OF THE IMAGE: Diffuse MCD MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 1.0000 Prob. Diffuse MCD: 0.0000 Prob. PVNH: 0.0000
REAL CLASSIFICATION OF THE IMAGE: Diffuse MCD MODEL CLASSIFICATION OF THE IMAGE: Diffuse MCD Prob. Normal MRI: 0.4641 Prob. Diffuse MCD: 0.5321 Prob. PVNH: 0.0038
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 1.0000 Prob. Diffuse MCD: 0.0000 Prob. PVNH: 0.0000
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 1.0000 Prob. Diffuse MCD: 0.0000 Prob. PVNH: 0.0000
REAL CLASSIFICATION OF THE IMAGE: PVNH MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 0.9998 Prob. Diffuse MCD: 0.0000 Prob. PVNH: 0.0002
REAL CLASSIFICATION OF THE IMAGE: PVNH MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 0.9792 Prob. Diffuse MCD: 0.0002 Prob. PVNH: 0.0207
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Diffuse MCD Prob. Normal MRI: 0.0109 Prob. Diffuse MCD: 0.9890 Prob. PVNH: 0.0000
REAL CLASSIFICATION OF THE IMAGE: Diffuse MCD MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 0.9991 Prob. Diffuse MCD: 0.0009 Prob. PVNH: 0.0000
REAL CLASSIFICATION OF THE IMAGE: Diffuse MCD MODEL CLASSIFICATION OF THE IMAGE: Diffuse MCD Prob. Normal MRI: 0.0000 Prob. Diffuse MCD: 1.0000 Prob. PVNH: 0.0000
REAL CLASSIFICATION OF THE IMAGE: Diffuse MCD MODEL CLASSIFICATION OF THE IMAGE: Diffuse MCD Prob. Normal MRI: 0.4796 Prob. Diffuse MCD: 0.5202 Prob. PVNH: 0.0001
REAL CLASSIFICATION OF THE IMAGE: Diffuse MCD MODEL CLASSIFICATION OF THE IMAGE: PVNH Prob. Normal MRI: 0.2447 Prob. Diffuse MCD: 0.2761 Prob. PVNH: 0.4792
REAL CLASSIFICATION OF THE IMAGE: Diffuse MCD MODEL CLASSIFICATION OF THE IMAGE: Diffuse MCD Prob. Normal MRI: 0.1568 Prob. Diffuse MCD: 0.8425 Prob. PVNH: 0.0007
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 0.6941 Prob. Diffuse MCD: 0.2739 Prob. PVNH: 0.0320
REAL CLASSIFICATION OF THE IMAGE: Diffuse MCD MODEL CLASSIFICATION OF THE IMAGE: Diffuse MCD Prob. Normal MRI: 0.0017 Prob. Diffuse MCD: 0.9983 Prob. PVNH: 0.0001
InceptionResNetV2¶
Define the convolutional neural network¶
In [56]:
# Use InceptionResNetV2 as the base model
base_model = tf.keras.applications.inception_resnet_v2.InceptionResNetV2(layers=tf.keras.layers, weights='imagenet', include_top = False, input_shape=train_X.shape[1:])
# Get the output of the base model
x = base_model.output
# Add a 2D global average pooling layer
x = GlobalAveragePooling2D()(x)
## Add the fully-connected layers
x = Dense(units=516, activation='relu')(x)
x = Dropout(rate=0.5)(x)
x = Dense(units=256, activation='relu')(x)
x = Dropout(rate=0.5)(x)
x = Dense(units=64, activation='relu')(x)
# Ad a layer for multiclass classification
predictions = Dense(units = 3, activation = 'softmax')(x)
# Define the model to be trained
model = Model(inputs = base_model.input, outputs = predictions)
# Train the last 75 layers in the base model
for layer in base_model.layers[:-75]:
layer.trainable = False
for layer in base_model.layers[-75:]:
layer.trainable = True
# Compile the model
opt = Adam(lr = 0.0001)
model.compile(optimizer = opt, loss = 'categorical_crossentropy', metrics = ['accuracy'])
# Fit and test the model in the validation set
historyResNet50 = model.fit(shuffled_train_X, shuffled_train_y, validation_data = [shuffled_val_X, shuffled_val_y], epochs = 50, batch_size = 32)
print('\n')
print('\n')
# AUC in train and validation set
auc_trainResNet50 = roc_auc_score(shuffled_train_y, model.predict(shuffled_train_X))
print('The AUC in the train set is {:.4f}.'.format(auc_trainResNet50))
print('\n')
print('\n')
auc_validResNet50 = roc_auc_score(shuffled_val_y, model.predict(shuffled_val_X))
print('The AUC in the validation set is {:.4f}.'.format(auc_validResNet50))
print('\n')
print('\n')
print('\n')
print('\n')
# Figure size and colors
mpl.rcParams['figure.figsize'] = (20,24)
plt.rcParams['axes.facecolor']='white'
plt.rcParams['figure.facecolor']='lightgreen'
plt.rcParams['figure.edgecolor']='black'
# Plot history of loss during training
plt.plot(historyResNet50.history['loss'], label='Train', color='red')
plt.plot(historyResNet50.history['val_loss'], label='Validation', color='blue')
plt.title('Loss in train and validation set', size=30)
plt.xlabel('Epoch', size=24)
plt.ylabel('Categorical cross-entropy loss', size=24)
plt.xticks(fontsize=20)
plt.yticks(fontsize=20)
plt.legend(fontsize=20)
plt.ylim(ymin=0, ymax=5)
plt.grid(b=None)
plt.show()
print('\n')
print('\n')
print('\n')
print('\n')
# Plot history of accuracy
plt.plot(historyResNet50.history['accuracy'], label='Train', color='red')
plt.plot(historyResNet50.history['val_accuracy'], label='Validation', color='blue')
plt.title('Accuracy in train and validation set', size=30)
plt.xlabel('Epoch', size=24)
plt.ylabel('Accuracy', size=24)
plt.xticks(fontsize=20)
plt.yticks(fontsize=20)
plt.legend(fontsize=20)
plt.ylim(ymin=0, ymax=1.1)
plt.grid(b=None)
plt.show()
Train on 8108 samples, validate on 481 samples Epoch 1/50 8108/8108 [==============================] - 104s 13ms/sample - loss: 0.4667 - accuracy: 0.7985 - val_loss: 1.3852 - val_accuracy: 0.6466 Epoch 2/50 8108/8108 [==============================] - 91s 11ms/sample - loss: 0.0861 - accuracy: 0.9720 - val_loss: 1.5463 - val_accuracy: 0.6694 Epoch 3/50 8108/8108 [==============================] - 95s 12ms/sample - loss: 0.0526 - accuracy: 0.9816 - val_loss: 1.5635 - val_accuracy: 0.7110 Epoch 4/50 8108/8108 [==============================] - 93s 11ms/sample - loss: 0.0326 - accuracy: 0.9894 - val_loss: 1.6076 - val_accuracy: 0.7214 Epoch 5/50 8108/8108 [==============================] - 93s 11ms/sample - loss: 0.0451 - accuracy: 0.9850 - val_loss: 1.9910 - val_accuracy: 0.6944 Epoch 6/50 8108/8108 [==============================] - 91s 11ms/sample - loss: 0.0216 - accuracy: 0.9933 - val_loss: 2.5251 - val_accuracy: 0.6029 Epoch 7/50 8108/8108 [==============================] - 94s 12ms/sample - loss: 0.0229 - accuracy: 0.9938 - val_loss: 1.7570 - val_accuracy: 0.6881 Epoch 8/50 8108/8108 [==============================] - 92s 11ms/sample - loss: 0.0197 - accuracy: 0.9935 - val_loss: 1.6649 - val_accuracy: 0.7152 Epoch 9/50 8108/8108 [==============================] - 93s 11ms/sample - loss: 0.0296 - accuracy: 0.9915 - val_loss: 1.5648 - val_accuracy: 0.7048 Epoch 10/50 8108/8108 [==============================] - 93s 12ms/sample - loss: 0.0132 - accuracy: 0.9953 - val_loss: 2.0842 - val_accuracy: 0.6902 Epoch 11/50 8108/8108 [==============================] - 91s 11ms/sample - loss: 0.0111 - accuracy: 0.9967 - val_loss: 2.3350 - val_accuracy: 0.6965 Epoch 12/50 8108/8108 [==============================] - 94s 12ms/sample - loss: 0.0203 - accuracy: 0.9943 - val_loss: 1.7271 - val_accuracy: 0.7152 Epoch 13/50 8108/8108 [==============================] - 92s 11ms/sample - loss: 0.0116 - accuracy: 0.9967 - val_loss: 1.9950 - val_accuracy: 0.7048 Epoch 14/50 8108/8108 [==============================] - 91s 11ms/sample - loss: 0.0207 - accuracy: 0.9933 - val_loss: 2.4622 - val_accuracy: 0.6362 Epoch 15/50 8108/8108 [==============================] - 94s 12ms/sample - loss: 0.0131 - accuracy: 0.9953 - val_loss: 2.2062 - val_accuracy: 0.6985 Epoch 16/50 8108/8108 [==============================] - 92s 11ms/sample - loss: 0.0119 - accuracy: 0.9962 - val_loss: 1.7077 - val_accuracy: 0.7152 Epoch 17/50 8108/8108 [==============================] - 92s 11ms/sample - loss: 0.0071 - accuracy: 0.9968 - val_loss: 2.6881 - val_accuracy: 0.6902 Epoch 18/50 8108/8108 [==============================] - 94s 12ms/sample - loss: 0.0032 - accuracy: 0.9988 - val_loss: 2.7369 - val_accuracy: 0.6715 Epoch 19/50 8108/8108 [==============================] - 91s 11ms/sample - loss: 0.0175 - accuracy: 0.9944 - val_loss: 2.3236 - val_accuracy: 0.6445 Epoch 20/50 8108/8108 [==============================] - 93s 12ms/sample - loss: 0.0146 - accuracy: 0.9959 - val_loss: 2.2163 - val_accuracy: 0.6570 Epoch 21/50 8108/8108 [==============================] - 92s 11ms/sample - loss: 0.0088 - accuracy: 0.9975 - val_loss: 2.2587 - val_accuracy: 0.6778 Epoch 22/50 8108/8108 [==============================] - 91s 11ms/sample - loss: 0.0043 - accuracy: 0.9989 - val_loss: 2.8002 - val_accuracy: 0.6881 Epoch 23/50 8108/8108 [==============================] - 92s 11ms/sample - loss: 0.0089 - accuracy: 0.9972 - val_loss: 2.0520 - val_accuracy: 0.6819 Epoch 24/50 8108/8108 [==============================] - 93s 12ms/sample - loss: 0.0105 - accuracy: 0.9972 - val_loss: 2.9960 - val_accuracy: 0.6590 Epoch 25/50 8108/8108 [==============================] - 91s 11ms/sample - loss: 0.0119 - accuracy: 0.9970 - val_loss: 1.8573 - val_accuracy: 0.7131 Epoch 26/50 8108/8108 [==============================] - 90s 11ms/sample - loss: 0.0083 - accuracy: 0.9978 - val_loss: 2.7245 - val_accuracy: 0.6112 Epoch 27/50 8108/8108 [==============================] - 93s 11ms/sample - loss: 0.0194 - accuracy: 0.9935 - val_loss: 2.3681 - val_accuracy: 0.6632 Epoch 28/50 8108/8108 [==============================] - 93s 12ms/sample - loss: 0.0099 - accuracy: 0.9968 - val_loss: 1.8456 - val_accuracy: 0.6694 Epoch 29/50 8108/8108 [==============================] - 90s 11ms/sample - loss: 0.0023 - accuracy: 0.9993 - val_loss: 2.0500 - val_accuracy: 0.6840 Epoch 30/50 8108/8108 [==============================] - 91s 11ms/sample - loss: 4.3919e-04 - accuracy: 0.9999 - val_loss: 2.3009 - val_accuracy: 0.6965 Epoch 31/50 8108/8108 [==============================] - 94s 12ms/sample - loss: 0.0098 - accuracy: 0.9964 - val_loss: 2.4320 - val_accuracy: 0.6965 Epoch 32/50 8108/8108 [==============================] - 92s 11ms/sample - loss: 0.0105 - accuracy: 0.9964 - val_loss: 2.3082 - val_accuracy: 0.6694 Epoch 33/50 8108/8108 [==============================] - 91s 11ms/sample - loss: 0.0094 - accuracy: 0.9969 - val_loss: 2.1126 - val_accuracy: 0.7069 Epoch 34/50 8108/8108 [==============================] - 91s 11ms/sample - loss: 0.0017 - accuracy: 0.9994 - val_loss: 2.3758 - val_accuracy: 0.7173 Epoch 35/50 8108/8108 [==============================] - 93s 11ms/sample - loss: 0.0096 - accuracy: 0.9977 - val_loss: 2.1160 - val_accuracy: 0.6507 Epoch 36/50 8108/8108 [==============================] - 93s 11ms/sample - loss: 0.0059 - accuracy: 0.9979 - val_loss: 2.3858 - val_accuracy: 0.6611 Epoch 37/50 8108/8108 [==============================] - 90s 11ms/sample - loss: 0.0112 - accuracy: 0.9972 - val_loss: 2.0203 - val_accuracy: 0.6840 Epoch 38/50 8108/8108 [==============================] - 90s 11ms/sample - loss: 0.0125 - accuracy: 0.9968 - val_loss: 2.5575 - val_accuracy: 0.6570 Epoch 39/50 8108/8108 [==============================] - 91s 11ms/sample - loss: 0.0051 - accuracy: 0.9984 - val_loss: 2.4495 - val_accuracy: 0.7193 Epoch 40/50 8108/8108 [==============================] - 94s 12ms/sample - loss: 0.0037 - accuracy: 0.9990 - val_loss: 2.0446 - val_accuracy: 0.7027 Epoch 41/50 8108/8108 [==============================] - 92s 11ms/sample - loss: 0.0147 - accuracy: 0.9956 - val_loss: 2.7518 - val_accuracy: 0.6403 Epoch 42/50 8108/8108 [==============================] - 92s 11ms/sample - loss: 0.0066 - accuracy: 0.9980 - val_loss: 2.2613 - val_accuracy: 0.6819 Epoch 43/50 8108/8108 [==============================] - 92s 11ms/sample - loss: 0.0051 - accuracy: 0.9984 - val_loss: 2.0904 - val_accuracy: 0.6778 Epoch 44/50 8108/8108 [==============================] - 94s 12ms/sample - loss: 4.2518e-04 - accuracy: 0.9999 - val_loss: 2.4416 - val_accuracy: 0.6715 Epoch 45/50 8108/8108 [==============================] - 91s 11ms/sample - loss: 0.0033 - accuracy: 0.9990 - val_loss: 2.5068 - val_accuracy: 0.6632 Epoch 46/50 8108/8108 [==============================] - 92s 11ms/sample - loss: 0.0101 - accuracy: 0.9968 - val_loss: 2.7600 - val_accuracy: 0.6445 Epoch 47/50 8108/8108 [==============================] - 94s 12ms/sample - loss: 0.0090 - accuracy: 0.9978 - val_loss: 2.1089 - val_accuracy: 0.6902 Epoch 48/50 8108/8108 [==============================] - 91s 11ms/sample - loss: 0.0046 - accuracy: 0.9988 - val_loss: 2.2747 - val_accuracy: 0.7006 Epoch 49/50 8108/8108 [==============================] - 92s 11ms/sample - loss: 0.0037 - accuracy: 0.9991 - val_loss: 2.0968 - val_accuracy: 0.7173 Epoch 50/50 8108/8108 [==============================] - 91s 11ms/sample - loss: 2.1173e-04 - accuracy: 1.0000 - val_loss: 2.1230 - val_accuracy: 0.7214 The AUC in the train set is 1.0000. The AUC in the validation set is 0.8670.
In [57]:
# Generate predictions in the form of probabilities for the validation set
valInceptionResNetV2 = model.predict(shuffled_val_X, batch_size = 32)
# Generate the confusion matrix in the validation set
y_true = np.argmax(shuffled_val_y, axis=1)
y_predInceptionResNetV2 = np.argmax(valInceptionResNetV2, axis=1)
# Confusion matrix
pd.DataFrame(confusion_matrix(y_true, y_predInceptionResNetV2), index=['True: Normal', 'True: Diffuse CM', 'True: PVNH'], columns=['Prediction: Normal', 'Prediction: Diffuse CM', 'Prediction: PVNH']).T
Out[57]:
| True: Normal | True: Diffuse CM | True: PVNH | |
|---|---|---|---|
| Prediction: Normal | 116 | 40 | 27 |
| Prediction: Diffuse CM | 11 | 101 | 18 |
| Prediction: PVNH | 32 | 6 | 130 |
In [58]:
# Calculate accuracy in the validation set
accuracy_InceptionResNetV2 = accuracy_score(y_true=y_true, y_pred=y_predInceptionResNetV2)
print('The accuracy in the validation set is {:.4f}.'.format(accuracy_InceptionResNetV2))
The accuracy in the validation set is 0.7214.
In [59]:
# Calculate AUC in the validation set
auc_validInceptionResNetV2 = roc_auc_score(shuffled_val_y, model.predict(shuffled_val_X))
print('The AUC in the validation set is {:.4f}.'.format(auc_validInceptionResNetV2))
The AUC in the validation set is 0.8670.
In [60]:
# Classification report
print(classification_report(y_true, y_predInceptionResNetV2, target_names=['Normal MRI', 'Diffuse CM', 'PVNH']))
precision recall f1-score support
Normal MRI 0.63 0.73 0.68 159
Diffuse CM 0.78 0.69 0.73 147
PVNH 0.77 0.74 0.76 175
accuracy 0.72 481
macro avg 0.73 0.72 0.72 481
weighted avg 0.73 0.72 0.72 481
Save model InceptionResNetV2¶
In [61]:
# Serialize model to JSON
model_json = model.to_json()
with open("InceptionResNetV2.json", "w") as json_file:
json_file.write(model_json)
# Serialize weights to HDF5
model.save_weights("InceptionResNetV2.h5")
Model visualization¶
In [62]:
# Visualize the structure and layers of the model
model.layers
Out[62]:
[<tensorflow.python.keras.engine.input_layer.InputLayer at 0x7f226432d7f0>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f226432d8d0>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f226432de48>, <tensorflow.python.keras.layers.core.Activation at 0x7f226432de10>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f21503ff240>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f215040ef28>, <tensorflow.python.keras.layers.core.Activation at 0x7f215040eef0>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f21504133c8>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f21344bccc0>, <tensorflow.python.keras.layers.core.Activation at 0x7f21344c3390>, <tensorflow.python.keras.layers.pooling.MaxPooling2D at 0x7f21344c3128>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f21344c3470>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f21344ee748>, <tensorflow.python.keras.layers.core.Activation at 0x7f21344eed30>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f21344ee4e0>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f213449aa90>, <tensorflow.python.keras.layers.core.Activation at 0x7f21344a1080>, <tensorflow.python.keras.layers.pooling.MaxPooling2D at 0x7f213449a748>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f2134427438>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f21343cea90>, <tensorflow.python.keras.layers.core.Activation at 0x7f21343d4080>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f213444d160>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f21343ce748>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f2134470ef0>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f213437bda0>, <tensorflow.python.keras.layers.core.Activation at 0x7f21343fa9e8>, 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<tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f2114797cf8>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f21146fc748>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f211479dc50>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f2114729cc0>, <tensorflow.python.keras.layers.core.Activation at 0x7f21147a5be0>, <tensorflow.python.keras.layers.core.Activation at 0x7f2114729c88>, <tensorflow.python.keras.layers.merge.Concatenate at 0x7f2114729f28>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f2114729fd0>, <tensorflow.python.keras.layers.core.Lambda at 0x7f21146d4e48>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f21146d4f28>, <tensorflow.python.keras.layers.normalization_v2.BatchNormalization at 0x7f21146dcef0>, <tensorflow.python.keras.layers.core.Activation at 0x7f21146e6ac8>, <tensorflow.python.keras.layers.pooling.GlobalAveragePooling2D at 0x7f21145bb438>, <tensorflow.python.keras.layers.core.Dense at 0x7f226432d7b8>, <tensorflow.python.keras.layers.core.Dropout at 0x7f21145bb4e0>, <tensorflow.python.keras.layers.core.Dense at 0x7f2114567f60>, <tensorflow.python.keras.layers.core.Dropout at 0x7f2114567b00>, <tensorflow.python.keras.layers.core.Dense at 0x7f21145a7240>, <tensorflow.python.keras.layers.core.Dense at 0x7f211453f9e8>]
In [63]:
# Visualize the structure and layers of the model
print(model.summary())
Model: "model_183"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_4 (InputLayer) [(None, 512, 512, 3) 0
__________________________________________________________________________________________________
conv2d_129 (Conv2D) (None, 255, 255, 32) 864 input_4[0][0]
__________________________________________________________________________________________________
batch_normalization_98 (BatchNo (None, 255, 255, 32) 96 conv2d_129[0][0]
__________________________________________________________________________________________________
activation_98 (Activation) (None, 255, 255, 32) 0 batch_normalization_98[0][0]
__________________________________________________________________________________________________
conv2d_130 (Conv2D) (None, 253, 253, 32) 9216 activation_98[0][0]
__________________________________________________________________________________________________
batch_normalization_99 (BatchNo (None, 253, 253, 32) 96 conv2d_130[0][0]
__________________________________________________________________________________________________
activation_99 (Activation) (None, 253, 253, 32) 0 batch_normalization_99[0][0]
__________________________________________________________________________________________________
conv2d_131 (Conv2D) (None, 253, 253, 64) 18432 activation_99[0][0]
__________________________________________________________________________________________________
batch_normalization_100 (BatchN (None, 253, 253, 64) 192 conv2d_131[0][0]
__________________________________________________________________________________________________
activation_100 (Activation) (None, 253, 253, 64) 0 batch_normalization_100[0][0]
__________________________________________________________________________________________________
max_pooling2d_14 (MaxPooling2D) (None, 126, 126, 64) 0 activation_100[0][0]
__________________________________________________________________________________________________
conv2d_132 (Conv2D) (None, 126, 126, 80) 5120 max_pooling2d_14[0][0]
__________________________________________________________________________________________________
batch_normalization_101 (BatchN (None, 126, 126, 80) 240 conv2d_132[0][0]
__________________________________________________________________________________________________
activation_101 (Activation) (None, 126, 126, 80) 0 batch_normalization_101[0][0]
__________________________________________________________________________________________________
conv2d_133 (Conv2D) (None, 124, 124, 192 138240 activation_101[0][0]
__________________________________________________________________________________________________
batch_normalization_102 (BatchN (None, 124, 124, 192 576 conv2d_133[0][0]
__________________________________________________________________________________________________
activation_102 (Activation) (None, 124, 124, 192 0 batch_normalization_102[0][0]
__________________________________________________________________________________________________
max_pooling2d_15 (MaxPooling2D) (None, 61, 61, 192) 0 activation_102[0][0]
__________________________________________________________________________________________________
conv2d_137 (Conv2D) (None, 61, 61, 64) 12288 max_pooling2d_15[0][0]
__________________________________________________________________________________________________
batch_normalization_106 (BatchN (None, 61, 61, 64) 192 conv2d_137[0][0]
__________________________________________________________________________________________________
activation_106 (Activation) (None, 61, 61, 64) 0 batch_normalization_106[0][0]
__________________________________________________________________________________________________
conv2d_135 (Conv2D) (None, 61, 61, 48) 9216 max_pooling2d_15[0][0]
__________________________________________________________________________________________________
conv2d_138 (Conv2D) (None, 61, 61, 96) 55296 activation_106[0][0]
__________________________________________________________________________________________________
batch_normalization_104 (BatchN (None, 61, 61, 48) 144 conv2d_135[0][0]
__________________________________________________________________________________________________
batch_normalization_107 (BatchN (None, 61, 61, 96) 288 conv2d_138[0][0]
__________________________________________________________________________________________________
activation_104 (Activation) (None, 61, 61, 48) 0 batch_normalization_104[0][0]
__________________________________________________________________________________________________
activation_107 (Activation) (None, 61, 61, 96) 0 batch_normalization_107[0][0]
__________________________________________________________________________________________________
average_pooling2d_9 (AveragePoo (None, 61, 61, 192) 0 max_pooling2d_15[0][0]
__________________________________________________________________________________________________
conv2d_134 (Conv2D) (None, 61, 61, 96) 18432 max_pooling2d_15[0][0]
__________________________________________________________________________________________________
conv2d_136 (Conv2D) (None, 61, 61, 64) 76800 activation_104[0][0]
__________________________________________________________________________________________________
conv2d_139 (Conv2D) (None, 61, 61, 96) 82944 activation_107[0][0]
__________________________________________________________________________________________________
conv2d_140 (Conv2D) (None, 61, 61, 64) 12288 average_pooling2d_9[0][0]
__________________________________________________________________________________________________
batch_normalization_103 (BatchN (None, 61, 61, 96) 288 conv2d_134[0][0]
__________________________________________________________________________________________________
batch_normalization_105 (BatchN (None, 61, 61, 64) 192 conv2d_136[0][0]
__________________________________________________________________________________________________
batch_normalization_108 (BatchN (None, 61, 61, 96) 288 conv2d_139[0][0]
__________________________________________________________________________________________________
batch_normalization_109 (BatchN (None, 61, 61, 64) 192 conv2d_140[0][0]
__________________________________________________________________________________________________
activation_103 (Activation) (None, 61, 61, 96) 0 batch_normalization_103[0][0]
__________________________________________________________________________________________________
activation_105 (Activation) (None, 61, 61, 64) 0 batch_normalization_105[0][0]
__________________________________________________________________________________________________
activation_108 (Activation) (None, 61, 61, 96) 0 batch_normalization_108[0][0]
__________________________________________________________________________________________________
activation_109 (Activation) (None, 61, 61, 64) 0 batch_normalization_109[0][0]
__________________________________________________________________________________________________
mixed_5b (Concatenate) (None, 61, 61, 320) 0 activation_103[0][0]
activation_105[0][0]
activation_108[0][0]
activation_109[0][0]
__________________________________________________________________________________________________
conv2d_144 (Conv2D) (None, 61, 61, 32) 10240 mixed_5b[0][0]
__________________________________________________________________________________________________
batch_normalization_113 (BatchN (None, 61, 61, 32) 96 conv2d_144[0][0]
__________________________________________________________________________________________________
activation_113 (Activation) (None, 61, 61, 32) 0 batch_normalization_113[0][0]
__________________________________________________________________________________________________
conv2d_142 (Conv2D) (None, 61, 61, 32) 10240 mixed_5b[0][0]
__________________________________________________________________________________________________
conv2d_145 (Conv2D) (None, 61, 61, 48) 13824 activation_113[0][0]
__________________________________________________________________________________________________
batch_normalization_111 (BatchN (None, 61, 61, 32) 96 conv2d_142[0][0]
__________________________________________________________________________________________________
batch_normalization_114 (BatchN (None, 61, 61, 48) 144 conv2d_145[0][0]
__________________________________________________________________________________________________
activation_111 (Activation) (None, 61, 61, 32) 0 batch_normalization_111[0][0]
__________________________________________________________________________________________________
activation_114 (Activation) (None, 61, 61, 48) 0 batch_normalization_114[0][0]
__________________________________________________________________________________________________
conv2d_141 (Conv2D) (None, 61, 61, 32) 10240 mixed_5b[0][0]
__________________________________________________________________________________________________
conv2d_143 (Conv2D) (None, 61, 61, 32) 9216 activation_111[0][0]
__________________________________________________________________________________________________
conv2d_146 (Conv2D) (None, 61, 61, 64) 27648 activation_114[0][0]
__________________________________________________________________________________________________
batch_normalization_110 (BatchN (None, 61, 61, 32) 96 conv2d_141[0][0]
__________________________________________________________________________________________________
batch_normalization_112 (BatchN (None, 61, 61, 32) 96 conv2d_143[0][0]
__________________________________________________________________________________________________
batch_normalization_115 (BatchN (None, 61, 61, 64) 192 conv2d_146[0][0]
__________________________________________________________________________________________________
activation_110 (Activation) (None, 61, 61, 32) 0 batch_normalization_110[0][0]
__________________________________________________________________________________________________
activation_112 (Activation) (None, 61, 61, 32) 0 batch_normalization_112[0][0]
__________________________________________________________________________________________________
activation_115 (Activation) (None, 61, 61, 64) 0 batch_normalization_115[0][0]
__________________________________________________________________________________________________
block35_1_mixed (Concatenate) (None, 61, 61, 128) 0 activation_110[0][0]
activation_112[0][0]
activation_115[0][0]
__________________________________________________________________________________________________
block35_1_conv (Conv2D) (None, 61, 61, 320) 41280 block35_1_mixed[0][0]
__________________________________________________________________________________________________
block35_1 (Lambda) (None, 61, 61, 320) 0 mixed_5b[0][0]
block35_1_conv[0][0]
__________________________________________________________________________________________________
block35_1_ac (Activation) (None, 61, 61, 320) 0 block35_1[0][0]
__________________________________________________________________________________________________
conv2d_150 (Conv2D) (None, 61, 61, 32) 10240 block35_1_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_119 (BatchN (None, 61, 61, 32) 96 conv2d_150[0][0]
__________________________________________________________________________________________________
activation_119 (Activation) (None, 61, 61, 32) 0 batch_normalization_119[0][0]
__________________________________________________________________________________________________
conv2d_148 (Conv2D) (None, 61, 61, 32) 10240 block35_1_ac[0][0]
__________________________________________________________________________________________________
conv2d_151 (Conv2D) (None, 61, 61, 48) 13824 activation_119[0][0]
__________________________________________________________________________________________________
batch_normalization_117 (BatchN (None, 61, 61, 32) 96 conv2d_148[0][0]
__________________________________________________________________________________________________
batch_normalization_120 (BatchN (None, 61, 61, 48) 144 conv2d_151[0][0]
__________________________________________________________________________________________________
activation_117 (Activation) (None, 61, 61, 32) 0 batch_normalization_117[0][0]
__________________________________________________________________________________________________
activation_120 (Activation) (None, 61, 61, 48) 0 batch_normalization_120[0][0]
__________________________________________________________________________________________________
conv2d_147 (Conv2D) (None, 61, 61, 32) 10240 block35_1_ac[0][0]
__________________________________________________________________________________________________
conv2d_149 (Conv2D) (None, 61, 61, 32) 9216 activation_117[0][0]
__________________________________________________________________________________________________
conv2d_152 (Conv2D) (None, 61, 61, 64) 27648 activation_120[0][0]
__________________________________________________________________________________________________
batch_normalization_116 (BatchN (None, 61, 61, 32) 96 conv2d_147[0][0]
__________________________________________________________________________________________________
batch_normalization_118 (BatchN (None, 61, 61, 32) 96 conv2d_149[0][0]
__________________________________________________________________________________________________
batch_normalization_121 (BatchN (None, 61, 61, 64) 192 conv2d_152[0][0]
__________________________________________________________________________________________________
activation_116 (Activation) (None, 61, 61, 32) 0 batch_normalization_116[0][0]
__________________________________________________________________________________________________
activation_118 (Activation) (None, 61, 61, 32) 0 batch_normalization_118[0][0]
__________________________________________________________________________________________________
activation_121 (Activation) (None, 61, 61, 64) 0 batch_normalization_121[0][0]
__________________________________________________________________________________________________
block35_2_mixed (Concatenate) (None, 61, 61, 128) 0 activation_116[0][0]
activation_118[0][0]
activation_121[0][0]
__________________________________________________________________________________________________
block35_2_conv (Conv2D) (None, 61, 61, 320) 41280 block35_2_mixed[0][0]
__________________________________________________________________________________________________
block35_2 (Lambda) (None, 61, 61, 320) 0 block35_1_ac[0][0]
block35_2_conv[0][0]
__________________________________________________________________________________________________
block35_2_ac (Activation) (None, 61, 61, 320) 0 block35_2[0][0]
__________________________________________________________________________________________________
conv2d_156 (Conv2D) (None, 61, 61, 32) 10240 block35_2_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_125 (BatchN (None, 61, 61, 32) 96 conv2d_156[0][0]
__________________________________________________________________________________________________
activation_125 (Activation) (None, 61, 61, 32) 0 batch_normalization_125[0][0]
__________________________________________________________________________________________________
conv2d_154 (Conv2D) (None, 61, 61, 32) 10240 block35_2_ac[0][0]
__________________________________________________________________________________________________
conv2d_157 (Conv2D) (None, 61, 61, 48) 13824 activation_125[0][0]
__________________________________________________________________________________________________
batch_normalization_123 (BatchN (None, 61, 61, 32) 96 conv2d_154[0][0]
__________________________________________________________________________________________________
batch_normalization_126 (BatchN (None, 61, 61, 48) 144 conv2d_157[0][0]
__________________________________________________________________________________________________
activation_123 (Activation) (None, 61, 61, 32) 0 batch_normalization_123[0][0]
__________________________________________________________________________________________________
activation_126 (Activation) (None, 61, 61, 48) 0 batch_normalization_126[0][0]
__________________________________________________________________________________________________
conv2d_153 (Conv2D) (None, 61, 61, 32) 10240 block35_2_ac[0][0]
__________________________________________________________________________________________________
conv2d_155 (Conv2D) (None, 61, 61, 32) 9216 activation_123[0][0]
__________________________________________________________________________________________________
conv2d_158 (Conv2D) (None, 61, 61, 64) 27648 activation_126[0][0]
__________________________________________________________________________________________________
batch_normalization_122 (BatchN (None, 61, 61, 32) 96 conv2d_153[0][0]
__________________________________________________________________________________________________
batch_normalization_124 (BatchN (None, 61, 61, 32) 96 conv2d_155[0][0]
__________________________________________________________________________________________________
batch_normalization_127 (BatchN (None, 61, 61, 64) 192 conv2d_158[0][0]
__________________________________________________________________________________________________
activation_122 (Activation) (None, 61, 61, 32) 0 batch_normalization_122[0][0]
__________________________________________________________________________________________________
activation_124 (Activation) (None, 61, 61, 32) 0 batch_normalization_124[0][0]
__________________________________________________________________________________________________
activation_127 (Activation) (None, 61, 61, 64) 0 batch_normalization_127[0][0]
__________________________________________________________________________________________________
block35_3_mixed (Concatenate) (None, 61, 61, 128) 0 activation_122[0][0]
activation_124[0][0]
activation_127[0][0]
__________________________________________________________________________________________________
block35_3_conv (Conv2D) (None, 61, 61, 320) 41280 block35_3_mixed[0][0]
__________________________________________________________________________________________________
block35_3 (Lambda) (None, 61, 61, 320) 0 block35_2_ac[0][0]
block35_3_conv[0][0]
__________________________________________________________________________________________________
block35_3_ac (Activation) (None, 61, 61, 320) 0 block35_3[0][0]
__________________________________________________________________________________________________
conv2d_162 (Conv2D) (None, 61, 61, 32) 10240 block35_3_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_131 (BatchN (None, 61, 61, 32) 96 conv2d_162[0][0]
__________________________________________________________________________________________________
activation_131 (Activation) (None, 61, 61, 32) 0 batch_normalization_131[0][0]
__________________________________________________________________________________________________
conv2d_160 (Conv2D) (None, 61, 61, 32) 10240 block35_3_ac[0][0]
__________________________________________________________________________________________________
conv2d_163 (Conv2D) (None, 61, 61, 48) 13824 activation_131[0][0]
__________________________________________________________________________________________________
batch_normalization_129 (BatchN (None, 61, 61, 32) 96 conv2d_160[0][0]
__________________________________________________________________________________________________
batch_normalization_132 (BatchN (None, 61, 61, 48) 144 conv2d_163[0][0]
__________________________________________________________________________________________________
activation_129 (Activation) (None, 61, 61, 32) 0 batch_normalization_129[0][0]
__________________________________________________________________________________________________
activation_132 (Activation) (None, 61, 61, 48) 0 batch_normalization_132[0][0]
__________________________________________________________________________________________________
conv2d_159 (Conv2D) (None, 61, 61, 32) 10240 block35_3_ac[0][0]
__________________________________________________________________________________________________
conv2d_161 (Conv2D) (None, 61, 61, 32) 9216 activation_129[0][0]
__________________________________________________________________________________________________
conv2d_164 (Conv2D) (None, 61, 61, 64) 27648 activation_132[0][0]
__________________________________________________________________________________________________
batch_normalization_128 (BatchN (None, 61, 61, 32) 96 conv2d_159[0][0]
__________________________________________________________________________________________________
batch_normalization_130 (BatchN (None, 61, 61, 32) 96 conv2d_161[0][0]
__________________________________________________________________________________________________
batch_normalization_133 (BatchN (None, 61, 61, 64) 192 conv2d_164[0][0]
__________________________________________________________________________________________________
activation_128 (Activation) (None, 61, 61, 32) 0 batch_normalization_128[0][0]
__________________________________________________________________________________________________
activation_130 (Activation) (None, 61, 61, 32) 0 batch_normalization_130[0][0]
__________________________________________________________________________________________________
activation_133 (Activation) (None, 61, 61, 64) 0 batch_normalization_133[0][0]
__________________________________________________________________________________________________
block35_4_mixed (Concatenate) (None, 61, 61, 128) 0 activation_128[0][0]
activation_130[0][0]
activation_133[0][0]
__________________________________________________________________________________________________
block35_4_conv (Conv2D) (None, 61, 61, 320) 41280 block35_4_mixed[0][0]
__________________________________________________________________________________________________
block35_4 (Lambda) (None, 61, 61, 320) 0 block35_3_ac[0][0]
block35_4_conv[0][0]
__________________________________________________________________________________________________
block35_4_ac (Activation) (None, 61, 61, 320) 0 block35_4[0][0]
__________________________________________________________________________________________________
conv2d_168 (Conv2D) (None, 61, 61, 32) 10240 block35_4_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_137 (BatchN (None, 61, 61, 32) 96 conv2d_168[0][0]
__________________________________________________________________________________________________
activation_137 (Activation) (None, 61, 61, 32) 0 batch_normalization_137[0][0]
__________________________________________________________________________________________________
conv2d_166 (Conv2D) (None, 61, 61, 32) 10240 block35_4_ac[0][0]
__________________________________________________________________________________________________
conv2d_169 (Conv2D) (None, 61, 61, 48) 13824 activation_137[0][0]
__________________________________________________________________________________________________
batch_normalization_135 (BatchN (None, 61, 61, 32) 96 conv2d_166[0][0]
__________________________________________________________________________________________________
batch_normalization_138 (BatchN (None, 61, 61, 48) 144 conv2d_169[0][0]
__________________________________________________________________________________________________
activation_135 (Activation) (None, 61, 61, 32) 0 batch_normalization_135[0][0]
__________________________________________________________________________________________________
activation_138 (Activation) (None, 61, 61, 48) 0 batch_normalization_138[0][0]
__________________________________________________________________________________________________
conv2d_165 (Conv2D) (None, 61, 61, 32) 10240 block35_4_ac[0][0]
__________________________________________________________________________________________________
conv2d_167 (Conv2D) (None, 61, 61, 32) 9216 activation_135[0][0]
__________________________________________________________________________________________________
conv2d_170 (Conv2D) (None, 61, 61, 64) 27648 activation_138[0][0]
__________________________________________________________________________________________________
batch_normalization_134 (BatchN (None, 61, 61, 32) 96 conv2d_165[0][0]
__________________________________________________________________________________________________
batch_normalization_136 (BatchN (None, 61, 61, 32) 96 conv2d_167[0][0]
__________________________________________________________________________________________________
batch_normalization_139 (BatchN (None, 61, 61, 64) 192 conv2d_170[0][0]
__________________________________________________________________________________________________
activation_134 (Activation) (None, 61, 61, 32) 0 batch_normalization_134[0][0]
__________________________________________________________________________________________________
activation_136 (Activation) (None, 61, 61, 32) 0 batch_normalization_136[0][0]
__________________________________________________________________________________________________
activation_139 (Activation) (None, 61, 61, 64) 0 batch_normalization_139[0][0]
__________________________________________________________________________________________________
block35_5_mixed (Concatenate) (None, 61, 61, 128) 0 activation_134[0][0]
activation_136[0][0]
activation_139[0][0]
__________________________________________________________________________________________________
block35_5_conv (Conv2D) (None, 61, 61, 320) 41280 block35_5_mixed[0][0]
__________________________________________________________________________________________________
block35_5 (Lambda) (None, 61, 61, 320) 0 block35_4_ac[0][0]
block35_5_conv[0][0]
__________________________________________________________________________________________________
block35_5_ac (Activation) (None, 61, 61, 320) 0 block35_5[0][0]
__________________________________________________________________________________________________
conv2d_174 (Conv2D) (None, 61, 61, 32) 10240 block35_5_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_143 (BatchN (None, 61, 61, 32) 96 conv2d_174[0][0]
__________________________________________________________________________________________________
activation_143 (Activation) (None, 61, 61, 32) 0 batch_normalization_143[0][0]
__________________________________________________________________________________________________
conv2d_172 (Conv2D) (None, 61, 61, 32) 10240 block35_5_ac[0][0]
__________________________________________________________________________________________________
conv2d_175 (Conv2D) (None, 61, 61, 48) 13824 activation_143[0][0]
__________________________________________________________________________________________________
batch_normalization_141 (BatchN (None, 61, 61, 32) 96 conv2d_172[0][0]
__________________________________________________________________________________________________
batch_normalization_144 (BatchN (None, 61, 61, 48) 144 conv2d_175[0][0]
__________________________________________________________________________________________________
activation_141 (Activation) (None, 61, 61, 32) 0 batch_normalization_141[0][0]
__________________________________________________________________________________________________
activation_144 (Activation) (None, 61, 61, 48) 0 batch_normalization_144[0][0]
__________________________________________________________________________________________________
conv2d_171 (Conv2D) (None, 61, 61, 32) 10240 block35_5_ac[0][0]
__________________________________________________________________________________________________
conv2d_173 (Conv2D) (None, 61, 61, 32) 9216 activation_141[0][0]
__________________________________________________________________________________________________
conv2d_176 (Conv2D) (None, 61, 61, 64) 27648 activation_144[0][0]
__________________________________________________________________________________________________
batch_normalization_140 (BatchN (None, 61, 61, 32) 96 conv2d_171[0][0]
__________________________________________________________________________________________________
batch_normalization_142 (BatchN (None, 61, 61, 32) 96 conv2d_173[0][0]
__________________________________________________________________________________________________
batch_normalization_145 (BatchN (None, 61, 61, 64) 192 conv2d_176[0][0]
__________________________________________________________________________________________________
activation_140 (Activation) (None, 61, 61, 32) 0 batch_normalization_140[0][0]
__________________________________________________________________________________________________
activation_142 (Activation) (None, 61, 61, 32) 0 batch_normalization_142[0][0]
__________________________________________________________________________________________________
activation_145 (Activation) (None, 61, 61, 64) 0 batch_normalization_145[0][0]
__________________________________________________________________________________________________
block35_6_mixed (Concatenate) (None, 61, 61, 128) 0 activation_140[0][0]
activation_142[0][0]
activation_145[0][0]
__________________________________________________________________________________________________
block35_6_conv (Conv2D) (None, 61, 61, 320) 41280 block35_6_mixed[0][0]
__________________________________________________________________________________________________
block35_6 (Lambda) (None, 61, 61, 320) 0 block35_5_ac[0][0]
block35_6_conv[0][0]
__________________________________________________________________________________________________
block35_6_ac (Activation) (None, 61, 61, 320) 0 block35_6[0][0]
__________________________________________________________________________________________________
conv2d_180 (Conv2D) (None, 61, 61, 32) 10240 block35_6_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_149 (BatchN (None, 61, 61, 32) 96 conv2d_180[0][0]
__________________________________________________________________________________________________
activation_149 (Activation) (None, 61, 61, 32) 0 batch_normalization_149[0][0]
__________________________________________________________________________________________________
conv2d_178 (Conv2D) (None, 61, 61, 32) 10240 block35_6_ac[0][0]
__________________________________________________________________________________________________
conv2d_181 (Conv2D) (None, 61, 61, 48) 13824 activation_149[0][0]
__________________________________________________________________________________________________
batch_normalization_147 (BatchN (None, 61, 61, 32) 96 conv2d_178[0][0]
__________________________________________________________________________________________________
batch_normalization_150 (BatchN (None, 61, 61, 48) 144 conv2d_181[0][0]
__________________________________________________________________________________________________
activation_147 (Activation) (None, 61, 61, 32) 0 batch_normalization_147[0][0]
__________________________________________________________________________________________________
activation_150 (Activation) (None, 61, 61, 48) 0 batch_normalization_150[0][0]
__________________________________________________________________________________________________
conv2d_177 (Conv2D) (None, 61, 61, 32) 10240 block35_6_ac[0][0]
__________________________________________________________________________________________________
conv2d_179 (Conv2D) (None, 61, 61, 32) 9216 activation_147[0][0]
__________________________________________________________________________________________________
conv2d_182 (Conv2D) (None, 61, 61, 64) 27648 activation_150[0][0]
__________________________________________________________________________________________________
batch_normalization_146 (BatchN (None, 61, 61, 32) 96 conv2d_177[0][0]
__________________________________________________________________________________________________
batch_normalization_148 (BatchN (None, 61, 61, 32) 96 conv2d_179[0][0]
__________________________________________________________________________________________________
batch_normalization_151 (BatchN (None, 61, 61, 64) 192 conv2d_182[0][0]
__________________________________________________________________________________________________
activation_146 (Activation) (None, 61, 61, 32) 0 batch_normalization_146[0][0]
__________________________________________________________________________________________________
activation_148 (Activation) (None, 61, 61, 32) 0 batch_normalization_148[0][0]
__________________________________________________________________________________________________
activation_151 (Activation) (None, 61, 61, 64) 0 batch_normalization_151[0][0]
__________________________________________________________________________________________________
block35_7_mixed (Concatenate) (None, 61, 61, 128) 0 activation_146[0][0]
activation_148[0][0]
activation_151[0][0]
__________________________________________________________________________________________________
block35_7_conv (Conv2D) (None, 61, 61, 320) 41280 block35_7_mixed[0][0]
__________________________________________________________________________________________________
block35_7 (Lambda) (None, 61, 61, 320) 0 block35_6_ac[0][0]
block35_7_conv[0][0]
__________________________________________________________________________________________________
block35_7_ac (Activation) (None, 61, 61, 320) 0 block35_7[0][0]
__________________________________________________________________________________________________
conv2d_186 (Conv2D) (None, 61, 61, 32) 10240 block35_7_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_155 (BatchN (None, 61, 61, 32) 96 conv2d_186[0][0]
__________________________________________________________________________________________________
activation_155 (Activation) (None, 61, 61, 32) 0 batch_normalization_155[0][0]
__________________________________________________________________________________________________
conv2d_184 (Conv2D) (None, 61, 61, 32) 10240 block35_7_ac[0][0]
__________________________________________________________________________________________________
conv2d_187 (Conv2D) (None, 61, 61, 48) 13824 activation_155[0][0]
__________________________________________________________________________________________________
batch_normalization_153 (BatchN (None, 61, 61, 32) 96 conv2d_184[0][0]
__________________________________________________________________________________________________
batch_normalization_156 (BatchN (None, 61, 61, 48) 144 conv2d_187[0][0]
__________________________________________________________________________________________________
activation_153 (Activation) (None, 61, 61, 32) 0 batch_normalization_153[0][0]
__________________________________________________________________________________________________
activation_156 (Activation) (None, 61, 61, 48) 0 batch_normalization_156[0][0]
__________________________________________________________________________________________________
conv2d_183 (Conv2D) (None, 61, 61, 32) 10240 block35_7_ac[0][0]
__________________________________________________________________________________________________
conv2d_185 (Conv2D) (None, 61, 61, 32) 9216 activation_153[0][0]
__________________________________________________________________________________________________
conv2d_188 (Conv2D) (None, 61, 61, 64) 27648 activation_156[0][0]
__________________________________________________________________________________________________
batch_normalization_152 (BatchN (None, 61, 61, 32) 96 conv2d_183[0][0]
__________________________________________________________________________________________________
batch_normalization_154 (BatchN (None, 61, 61, 32) 96 conv2d_185[0][0]
__________________________________________________________________________________________________
batch_normalization_157 (BatchN (None, 61, 61, 64) 192 conv2d_188[0][0]
__________________________________________________________________________________________________
activation_152 (Activation) (None, 61, 61, 32) 0 batch_normalization_152[0][0]
__________________________________________________________________________________________________
activation_154 (Activation) (None, 61, 61, 32) 0 batch_normalization_154[0][0]
__________________________________________________________________________________________________
activation_157 (Activation) (None, 61, 61, 64) 0 batch_normalization_157[0][0]
__________________________________________________________________________________________________
block35_8_mixed (Concatenate) (None, 61, 61, 128) 0 activation_152[0][0]
activation_154[0][0]
activation_157[0][0]
__________________________________________________________________________________________________
block35_8_conv (Conv2D) (None, 61, 61, 320) 41280 block35_8_mixed[0][0]
__________________________________________________________________________________________________
block35_8 (Lambda) (None, 61, 61, 320) 0 block35_7_ac[0][0]
block35_8_conv[0][0]
__________________________________________________________________________________________________
block35_8_ac (Activation) (None, 61, 61, 320) 0 block35_8[0][0]
__________________________________________________________________________________________________
conv2d_192 (Conv2D) (None, 61, 61, 32) 10240 block35_8_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_161 (BatchN (None, 61, 61, 32) 96 conv2d_192[0][0]
__________________________________________________________________________________________________
activation_161 (Activation) (None, 61, 61, 32) 0 batch_normalization_161[0][0]
__________________________________________________________________________________________________
conv2d_190 (Conv2D) (None, 61, 61, 32) 10240 block35_8_ac[0][0]
__________________________________________________________________________________________________
conv2d_193 (Conv2D) (None, 61, 61, 48) 13824 activation_161[0][0]
__________________________________________________________________________________________________
batch_normalization_159 (BatchN (None, 61, 61, 32) 96 conv2d_190[0][0]
__________________________________________________________________________________________________
batch_normalization_162 (BatchN (None, 61, 61, 48) 144 conv2d_193[0][0]
__________________________________________________________________________________________________
activation_159 (Activation) (None, 61, 61, 32) 0 batch_normalization_159[0][0]
__________________________________________________________________________________________________
activation_162 (Activation) (None, 61, 61, 48) 0 batch_normalization_162[0][0]
__________________________________________________________________________________________________
conv2d_189 (Conv2D) (None, 61, 61, 32) 10240 block35_8_ac[0][0]
__________________________________________________________________________________________________
conv2d_191 (Conv2D) (None, 61, 61, 32) 9216 activation_159[0][0]
__________________________________________________________________________________________________
conv2d_194 (Conv2D) (None, 61, 61, 64) 27648 activation_162[0][0]
__________________________________________________________________________________________________
batch_normalization_158 (BatchN (None, 61, 61, 32) 96 conv2d_189[0][0]
__________________________________________________________________________________________________
batch_normalization_160 (BatchN (None, 61, 61, 32) 96 conv2d_191[0][0]
__________________________________________________________________________________________________
batch_normalization_163 (BatchN (None, 61, 61, 64) 192 conv2d_194[0][0]
__________________________________________________________________________________________________
activation_158 (Activation) (None, 61, 61, 32) 0 batch_normalization_158[0][0]
__________________________________________________________________________________________________
activation_160 (Activation) (None, 61, 61, 32) 0 batch_normalization_160[0][0]
__________________________________________________________________________________________________
activation_163 (Activation) (None, 61, 61, 64) 0 batch_normalization_163[0][0]
__________________________________________________________________________________________________
block35_9_mixed (Concatenate) (None, 61, 61, 128) 0 activation_158[0][0]
activation_160[0][0]
activation_163[0][0]
__________________________________________________________________________________________________
block35_9_conv (Conv2D) (None, 61, 61, 320) 41280 block35_9_mixed[0][0]
__________________________________________________________________________________________________
block35_9 (Lambda) (None, 61, 61, 320) 0 block35_8_ac[0][0]
block35_9_conv[0][0]
__________________________________________________________________________________________________
block35_9_ac (Activation) (None, 61, 61, 320) 0 block35_9[0][0]
__________________________________________________________________________________________________
conv2d_198 (Conv2D) (None, 61, 61, 32) 10240 block35_9_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_167 (BatchN (None, 61, 61, 32) 96 conv2d_198[0][0]
__________________________________________________________________________________________________
activation_167 (Activation) (None, 61, 61, 32) 0 batch_normalization_167[0][0]
__________________________________________________________________________________________________
conv2d_196 (Conv2D) (None, 61, 61, 32) 10240 block35_9_ac[0][0]
__________________________________________________________________________________________________
conv2d_199 (Conv2D) (None, 61, 61, 48) 13824 activation_167[0][0]
__________________________________________________________________________________________________
batch_normalization_165 (BatchN (None, 61, 61, 32) 96 conv2d_196[0][0]
__________________________________________________________________________________________________
batch_normalization_168 (BatchN (None, 61, 61, 48) 144 conv2d_199[0][0]
__________________________________________________________________________________________________
activation_165 (Activation) (None, 61, 61, 32) 0 batch_normalization_165[0][0]
__________________________________________________________________________________________________
activation_168 (Activation) (None, 61, 61, 48) 0 batch_normalization_168[0][0]
__________________________________________________________________________________________________
conv2d_195 (Conv2D) (None, 61, 61, 32) 10240 block35_9_ac[0][0]
__________________________________________________________________________________________________
conv2d_197 (Conv2D) (None, 61, 61, 32) 9216 activation_165[0][0]
__________________________________________________________________________________________________
conv2d_200 (Conv2D) (None, 61, 61, 64) 27648 activation_168[0][0]
__________________________________________________________________________________________________
batch_normalization_164 (BatchN (None, 61, 61, 32) 96 conv2d_195[0][0]
__________________________________________________________________________________________________
batch_normalization_166 (BatchN (None, 61, 61, 32) 96 conv2d_197[0][0]
__________________________________________________________________________________________________
batch_normalization_169 (BatchN (None, 61, 61, 64) 192 conv2d_200[0][0]
__________________________________________________________________________________________________
activation_164 (Activation) (None, 61, 61, 32) 0 batch_normalization_164[0][0]
__________________________________________________________________________________________________
activation_166 (Activation) (None, 61, 61, 32) 0 batch_normalization_166[0][0]
__________________________________________________________________________________________________
activation_169 (Activation) (None, 61, 61, 64) 0 batch_normalization_169[0][0]
__________________________________________________________________________________________________
block35_10_mixed (Concatenate) (None, 61, 61, 128) 0 activation_164[0][0]
activation_166[0][0]
activation_169[0][0]
__________________________________________________________________________________________________
block35_10_conv (Conv2D) (None, 61, 61, 320) 41280 block35_10_mixed[0][0]
__________________________________________________________________________________________________
block35_10 (Lambda) (None, 61, 61, 320) 0 block35_9_ac[0][0]
block35_10_conv[0][0]
__________________________________________________________________________________________________
block35_10_ac (Activation) (None, 61, 61, 320) 0 block35_10[0][0]
__________________________________________________________________________________________________
conv2d_202 (Conv2D) (None, 61, 61, 256) 81920 block35_10_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_171 (BatchN (None, 61, 61, 256) 768 conv2d_202[0][0]
__________________________________________________________________________________________________
activation_171 (Activation) (None, 61, 61, 256) 0 batch_normalization_171[0][0]
__________________________________________________________________________________________________
conv2d_203 (Conv2D) (None, 61, 61, 256) 589824 activation_171[0][0]
__________________________________________________________________________________________________
batch_normalization_172 (BatchN (None, 61, 61, 256) 768 conv2d_203[0][0]
__________________________________________________________________________________________________
activation_172 (Activation) (None, 61, 61, 256) 0 batch_normalization_172[0][0]
__________________________________________________________________________________________________
conv2d_201 (Conv2D) (None, 30, 30, 384) 1105920 block35_10_ac[0][0]
__________________________________________________________________________________________________
conv2d_204 (Conv2D) (None, 30, 30, 384) 884736 activation_172[0][0]
__________________________________________________________________________________________________
batch_normalization_170 (BatchN (None, 30, 30, 384) 1152 conv2d_201[0][0]
__________________________________________________________________________________________________
batch_normalization_173 (BatchN (None, 30, 30, 384) 1152 conv2d_204[0][0]
__________________________________________________________________________________________________
activation_170 (Activation) (None, 30, 30, 384) 0 batch_normalization_170[0][0]
__________________________________________________________________________________________________
activation_173 (Activation) (None, 30, 30, 384) 0 batch_normalization_173[0][0]
__________________________________________________________________________________________________
max_pooling2d_16 (MaxPooling2D) (None, 30, 30, 320) 0 block35_10_ac[0][0]
__________________________________________________________________________________________________
mixed_6a (Concatenate) (None, 30, 30, 1088) 0 activation_170[0][0]
activation_173[0][0]
max_pooling2d_16[0][0]
__________________________________________________________________________________________________
conv2d_206 (Conv2D) (None, 30, 30, 128) 139264 mixed_6a[0][0]
__________________________________________________________________________________________________
batch_normalization_175 (BatchN (None, 30, 30, 128) 384 conv2d_206[0][0]
__________________________________________________________________________________________________
activation_175 (Activation) (None, 30, 30, 128) 0 batch_normalization_175[0][0]
__________________________________________________________________________________________________
conv2d_207 (Conv2D) (None, 30, 30, 160) 143360 activation_175[0][0]
__________________________________________________________________________________________________
batch_normalization_176 (BatchN (None, 30, 30, 160) 480 conv2d_207[0][0]
__________________________________________________________________________________________________
activation_176 (Activation) (None, 30, 30, 160) 0 batch_normalization_176[0][0]
__________________________________________________________________________________________________
conv2d_205 (Conv2D) (None, 30, 30, 192) 208896 mixed_6a[0][0]
__________________________________________________________________________________________________
conv2d_208 (Conv2D) (None, 30, 30, 192) 215040 activation_176[0][0]
__________________________________________________________________________________________________
batch_normalization_174 (BatchN (None, 30, 30, 192) 576 conv2d_205[0][0]
__________________________________________________________________________________________________
batch_normalization_177 (BatchN (None, 30, 30, 192) 576 conv2d_208[0][0]
__________________________________________________________________________________________________
activation_174 (Activation) (None, 30, 30, 192) 0 batch_normalization_174[0][0]
__________________________________________________________________________________________________
activation_177 (Activation) (None, 30, 30, 192) 0 batch_normalization_177[0][0]
__________________________________________________________________________________________________
block17_1_mixed (Concatenate) (None, 30, 30, 384) 0 activation_174[0][0]
activation_177[0][0]
__________________________________________________________________________________________________
block17_1_conv (Conv2D) (None, 30, 30, 1088) 418880 block17_1_mixed[0][0]
__________________________________________________________________________________________________
block17_1 (Lambda) (None, 30, 30, 1088) 0 mixed_6a[0][0]
block17_1_conv[0][0]
__________________________________________________________________________________________________
block17_1_ac (Activation) (None, 30, 30, 1088) 0 block17_1[0][0]
__________________________________________________________________________________________________
conv2d_210 (Conv2D) (None, 30, 30, 128) 139264 block17_1_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_179 (BatchN (None, 30, 30, 128) 384 conv2d_210[0][0]
__________________________________________________________________________________________________
activation_179 (Activation) (None, 30, 30, 128) 0 batch_normalization_179[0][0]
__________________________________________________________________________________________________
conv2d_211 (Conv2D) (None, 30, 30, 160) 143360 activation_179[0][0]
__________________________________________________________________________________________________
batch_normalization_180 (BatchN (None, 30, 30, 160) 480 conv2d_211[0][0]
__________________________________________________________________________________________________
activation_180 (Activation) (None, 30, 30, 160) 0 batch_normalization_180[0][0]
__________________________________________________________________________________________________
conv2d_209 (Conv2D) (None, 30, 30, 192) 208896 block17_1_ac[0][0]
__________________________________________________________________________________________________
conv2d_212 (Conv2D) (None, 30, 30, 192) 215040 activation_180[0][0]
__________________________________________________________________________________________________
batch_normalization_178 (BatchN (None, 30, 30, 192) 576 conv2d_209[0][0]
__________________________________________________________________________________________________
batch_normalization_181 (BatchN (None, 30, 30, 192) 576 conv2d_212[0][0]
__________________________________________________________________________________________________
activation_178 (Activation) (None, 30, 30, 192) 0 batch_normalization_178[0][0]
__________________________________________________________________________________________________
activation_181 (Activation) (None, 30, 30, 192) 0 batch_normalization_181[0][0]
__________________________________________________________________________________________________
block17_2_mixed (Concatenate) (None, 30, 30, 384) 0 activation_178[0][0]
activation_181[0][0]
__________________________________________________________________________________________________
block17_2_conv (Conv2D) (None, 30, 30, 1088) 418880 block17_2_mixed[0][0]
__________________________________________________________________________________________________
block17_2 (Lambda) (None, 30, 30, 1088) 0 block17_1_ac[0][0]
block17_2_conv[0][0]
__________________________________________________________________________________________________
block17_2_ac (Activation) (None, 30, 30, 1088) 0 block17_2[0][0]
__________________________________________________________________________________________________
conv2d_214 (Conv2D) (None, 30, 30, 128) 139264 block17_2_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_183 (BatchN (None, 30, 30, 128) 384 conv2d_214[0][0]
__________________________________________________________________________________________________
activation_183 (Activation) (None, 30, 30, 128) 0 batch_normalization_183[0][0]
__________________________________________________________________________________________________
conv2d_215 (Conv2D) (None, 30, 30, 160) 143360 activation_183[0][0]
__________________________________________________________________________________________________
batch_normalization_184 (BatchN (None, 30, 30, 160) 480 conv2d_215[0][0]
__________________________________________________________________________________________________
activation_184 (Activation) (None, 30, 30, 160) 0 batch_normalization_184[0][0]
__________________________________________________________________________________________________
conv2d_213 (Conv2D) (None, 30, 30, 192) 208896 block17_2_ac[0][0]
__________________________________________________________________________________________________
conv2d_216 (Conv2D) (None, 30, 30, 192) 215040 activation_184[0][0]
__________________________________________________________________________________________________
batch_normalization_182 (BatchN (None, 30, 30, 192) 576 conv2d_213[0][0]
__________________________________________________________________________________________________
batch_normalization_185 (BatchN (None, 30, 30, 192) 576 conv2d_216[0][0]
__________________________________________________________________________________________________
activation_182 (Activation) (None, 30, 30, 192) 0 batch_normalization_182[0][0]
__________________________________________________________________________________________________
activation_185 (Activation) (None, 30, 30, 192) 0 batch_normalization_185[0][0]
__________________________________________________________________________________________________
block17_3_mixed (Concatenate) (None, 30, 30, 384) 0 activation_182[0][0]
activation_185[0][0]
__________________________________________________________________________________________________
block17_3_conv (Conv2D) (None, 30, 30, 1088) 418880 block17_3_mixed[0][0]
__________________________________________________________________________________________________
block17_3 (Lambda) (None, 30, 30, 1088) 0 block17_2_ac[0][0]
block17_3_conv[0][0]
__________________________________________________________________________________________________
block17_3_ac (Activation) (None, 30, 30, 1088) 0 block17_3[0][0]
__________________________________________________________________________________________________
conv2d_218 (Conv2D) (None, 30, 30, 128) 139264 block17_3_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_187 (BatchN (None, 30, 30, 128) 384 conv2d_218[0][0]
__________________________________________________________________________________________________
activation_187 (Activation) (None, 30, 30, 128) 0 batch_normalization_187[0][0]
__________________________________________________________________________________________________
conv2d_219 (Conv2D) (None, 30, 30, 160) 143360 activation_187[0][0]
__________________________________________________________________________________________________
batch_normalization_188 (BatchN (None, 30, 30, 160) 480 conv2d_219[0][0]
__________________________________________________________________________________________________
activation_188 (Activation) (None, 30, 30, 160) 0 batch_normalization_188[0][0]
__________________________________________________________________________________________________
conv2d_217 (Conv2D) (None, 30, 30, 192) 208896 block17_3_ac[0][0]
__________________________________________________________________________________________________
conv2d_220 (Conv2D) (None, 30, 30, 192) 215040 activation_188[0][0]
__________________________________________________________________________________________________
batch_normalization_186 (BatchN (None, 30, 30, 192) 576 conv2d_217[0][0]
__________________________________________________________________________________________________
batch_normalization_189 (BatchN (None, 30, 30, 192) 576 conv2d_220[0][0]
__________________________________________________________________________________________________
activation_186 (Activation) (None, 30, 30, 192) 0 batch_normalization_186[0][0]
__________________________________________________________________________________________________
activation_189 (Activation) (None, 30, 30, 192) 0 batch_normalization_189[0][0]
__________________________________________________________________________________________________
block17_4_mixed (Concatenate) (None, 30, 30, 384) 0 activation_186[0][0]
activation_189[0][0]
__________________________________________________________________________________________________
block17_4_conv (Conv2D) (None, 30, 30, 1088) 418880 block17_4_mixed[0][0]
__________________________________________________________________________________________________
block17_4 (Lambda) (None, 30, 30, 1088) 0 block17_3_ac[0][0]
block17_4_conv[0][0]
__________________________________________________________________________________________________
block17_4_ac (Activation) (None, 30, 30, 1088) 0 block17_4[0][0]
__________________________________________________________________________________________________
conv2d_222 (Conv2D) (None, 30, 30, 128) 139264 block17_4_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_191 (BatchN (None, 30, 30, 128) 384 conv2d_222[0][0]
__________________________________________________________________________________________________
activation_191 (Activation) (None, 30, 30, 128) 0 batch_normalization_191[0][0]
__________________________________________________________________________________________________
conv2d_223 (Conv2D) (None, 30, 30, 160) 143360 activation_191[0][0]
__________________________________________________________________________________________________
batch_normalization_192 (BatchN (None, 30, 30, 160) 480 conv2d_223[0][0]
__________________________________________________________________________________________________
activation_192 (Activation) (None, 30, 30, 160) 0 batch_normalization_192[0][0]
__________________________________________________________________________________________________
conv2d_221 (Conv2D) (None, 30, 30, 192) 208896 block17_4_ac[0][0]
__________________________________________________________________________________________________
conv2d_224 (Conv2D) (None, 30, 30, 192) 215040 activation_192[0][0]
__________________________________________________________________________________________________
batch_normalization_190 (BatchN (None, 30, 30, 192) 576 conv2d_221[0][0]
__________________________________________________________________________________________________
batch_normalization_193 (BatchN (None, 30, 30, 192) 576 conv2d_224[0][0]
__________________________________________________________________________________________________
activation_190 (Activation) (None, 30, 30, 192) 0 batch_normalization_190[0][0]
__________________________________________________________________________________________________
activation_193 (Activation) (None, 30, 30, 192) 0 batch_normalization_193[0][0]
__________________________________________________________________________________________________
block17_5_mixed (Concatenate) (None, 30, 30, 384) 0 activation_190[0][0]
activation_193[0][0]
__________________________________________________________________________________________________
block17_5_conv (Conv2D) (None, 30, 30, 1088) 418880 block17_5_mixed[0][0]
__________________________________________________________________________________________________
block17_5 (Lambda) (None, 30, 30, 1088) 0 block17_4_ac[0][0]
block17_5_conv[0][0]
__________________________________________________________________________________________________
block17_5_ac (Activation) (None, 30, 30, 1088) 0 block17_5[0][0]
__________________________________________________________________________________________________
conv2d_226 (Conv2D) (None, 30, 30, 128) 139264 block17_5_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_195 (BatchN (None, 30, 30, 128) 384 conv2d_226[0][0]
__________________________________________________________________________________________________
activation_195 (Activation) (None, 30, 30, 128) 0 batch_normalization_195[0][0]
__________________________________________________________________________________________________
conv2d_227 (Conv2D) (None, 30, 30, 160) 143360 activation_195[0][0]
__________________________________________________________________________________________________
batch_normalization_196 (BatchN (None, 30, 30, 160) 480 conv2d_227[0][0]
__________________________________________________________________________________________________
activation_196 (Activation) (None, 30, 30, 160) 0 batch_normalization_196[0][0]
__________________________________________________________________________________________________
conv2d_225 (Conv2D) (None, 30, 30, 192) 208896 block17_5_ac[0][0]
__________________________________________________________________________________________________
conv2d_228 (Conv2D) (None, 30, 30, 192) 215040 activation_196[0][0]
__________________________________________________________________________________________________
batch_normalization_194 (BatchN (None, 30, 30, 192) 576 conv2d_225[0][0]
__________________________________________________________________________________________________
batch_normalization_197 (BatchN (None, 30, 30, 192) 576 conv2d_228[0][0]
__________________________________________________________________________________________________
activation_194 (Activation) (None, 30, 30, 192) 0 batch_normalization_194[0][0]
__________________________________________________________________________________________________
activation_197 (Activation) (None, 30, 30, 192) 0 batch_normalization_197[0][0]
__________________________________________________________________________________________________
block17_6_mixed (Concatenate) (None, 30, 30, 384) 0 activation_194[0][0]
activation_197[0][0]
__________________________________________________________________________________________________
block17_6_conv (Conv2D) (None, 30, 30, 1088) 418880 block17_6_mixed[0][0]
__________________________________________________________________________________________________
block17_6 (Lambda) (None, 30, 30, 1088) 0 block17_5_ac[0][0]
block17_6_conv[0][0]
__________________________________________________________________________________________________
block17_6_ac (Activation) (None, 30, 30, 1088) 0 block17_6[0][0]
__________________________________________________________________________________________________
conv2d_230 (Conv2D) (None, 30, 30, 128) 139264 block17_6_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_199 (BatchN (None, 30, 30, 128) 384 conv2d_230[0][0]
__________________________________________________________________________________________________
activation_199 (Activation) (None, 30, 30, 128) 0 batch_normalization_199[0][0]
__________________________________________________________________________________________________
conv2d_231 (Conv2D) (None, 30, 30, 160) 143360 activation_199[0][0]
__________________________________________________________________________________________________
batch_normalization_200 (BatchN (None, 30, 30, 160) 480 conv2d_231[0][0]
__________________________________________________________________________________________________
activation_200 (Activation) (None, 30, 30, 160) 0 batch_normalization_200[0][0]
__________________________________________________________________________________________________
conv2d_229 (Conv2D) (None, 30, 30, 192) 208896 block17_6_ac[0][0]
__________________________________________________________________________________________________
conv2d_232 (Conv2D) (None, 30, 30, 192) 215040 activation_200[0][0]
__________________________________________________________________________________________________
batch_normalization_198 (BatchN (None, 30, 30, 192) 576 conv2d_229[0][0]
__________________________________________________________________________________________________
batch_normalization_201 (BatchN (None, 30, 30, 192) 576 conv2d_232[0][0]
__________________________________________________________________________________________________
activation_198 (Activation) (None, 30, 30, 192) 0 batch_normalization_198[0][0]
__________________________________________________________________________________________________
activation_201 (Activation) (None, 30, 30, 192) 0 batch_normalization_201[0][0]
__________________________________________________________________________________________________
block17_7_mixed (Concatenate) (None, 30, 30, 384) 0 activation_198[0][0]
activation_201[0][0]
__________________________________________________________________________________________________
block17_7_conv (Conv2D) (None, 30, 30, 1088) 418880 block17_7_mixed[0][0]
__________________________________________________________________________________________________
block17_7 (Lambda) (None, 30, 30, 1088) 0 block17_6_ac[0][0]
block17_7_conv[0][0]
__________________________________________________________________________________________________
block17_7_ac (Activation) (None, 30, 30, 1088) 0 block17_7[0][0]
__________________________________________________________________________________________________
conv2d_234 (Conv2D) (None, 30, 30, 128) 139264 block17_7_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_203 (BatchN (None, 30, 30, 128) 384 conv2d_234[0][0]
__________________________________________________________________________________________________
activation_203 (Activation) (None, 30, 30, 128) 0 batch_normalization_203[0][0]
__________________________________________________________________________________________________
conv2d_235 (Conv2D) (None, 30, 30, 160) 143360 activation_203[0][0]
__________________________________________________________________________________________________
batch_normalization_204 (BatchN (None, 30, 30, 160) 480 conv2d_235[0][0]
__________________________________________________________________________________________________
activation_204 (Activation) (None, 30, 30, 160) 0 batch_normalization_204[0][0]
__________________________________________________________________________________________________
conv2d_233 (Conv2D) (None, 30, 30, 192) 208896 block17_7_ac[0][0]
__________________________________________________________________________________________________
conv2d_236 (Conv2D) (None, 30, 30, 192) 215040 activation_204[0][0]
__________________________________________________________________________________________________
batch_normalization_202 (BatchN (None, 30, 30, 192) 576 conv2d_233[0][0]
__________________________________________________________________________________________________
batch_normalization_205 (BatchN (None, 30, 30, 192) 576 conv2d_236[0][0]
__________________________________________________________________________________________________
activation_202 (Activation) (None, 30, 30, 192) 0 batch_normalization_202[0][0]
__________________________________________________________________________________________________
activation_205 (Activation) (None, 30, 30, 192) 0 batch_normalization_205[0][0]
__________________________________________________________________________________________________
block17_8_mixed (Concatenate) (None, 30, 30, 384) 0 activation_202[0][0]
activation_205[0][0]
__________________________________________________________________________________________________
block17_8_conv (Conv2D) (None, 30, 30, 1088) 418880 block17_8_mixed[0][0]
__________________________________________________________________________________________________
block17_8 (Lambda) (None, 30, 30, 1088) 0 block17_7_ac[0][0]
block17_8_conv[0][0]
__________________________________________________________________________________________________
block17_8_ac (Activation) (None, 30, 30, 1088) 0 block17_8[0][0]
__________________________________________________________________________________________________
conv2d_238 (Conv2D) (None, 30, 30, 128) 139264 block17_8_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_207 (BatchN (None, 30, 30, 128) 384 conv2d_238[0][0]
__________________________________________________________________________________________________
activation_207 (Activation) (None, 30, 30, 128) 0 batch_normalization_207[0][0]
__________________________________________________________________________________________________
conv2d_239 (Conv2D) (None, 30, 30, 160) 143360 activation_207[0][0]
__________________________________________________________________________________________________
batch_normalization_208 (BatchN (None, 30, 30, 160) 480 conv2d_239[0][0]
__________________________________________________________________________________________________
activation_208 (Activation) (None, 30, 30, 160) 0 batch_normalization_208[0][0]
__________________________________________________________________________________________________
conv2d_237 (Conv2D) (None, 30, 30, 192) 208896 block17_8_ac[0][0]
__________________________________________________________________________________________________
conv2d_240 (Conv2D) (None, 30, 30, 192) 215040 activation_208[0][0]
__________________________________________________________________________________________________
batch_normalization_206 (BatchN (None, 30, 30, 192) 576 conv2d_237[0][0]
__________________________________________________________________________________________________
batch_normalization_209 (BatchN (None, 30, 30, 192) 576 conv2d_240[0][0]
__________________________________________________________________________________________________
activation_206 (Activation) (None, 30, 30, 192) 0 batch_normalization_206[0][0]
__________________________________________________________________________________________________
activation_209 (Activation) (None, 30, 30, 192) 0 batch_normalization_209[0][0]
__________________________________________________________________________________________________
block17_9_mixed (Concatenate) (None, 30, 30, 384) 0 activation_206[0][0]
activation_209[0][0]
__________________________________________________________________________________________________
block17_9_conv (Conv2D) (None, 30, 30, 1088) 418880 block17_9_mixed[0][0]
__________________________________________________________________________________________________
block17_9 (Lambda) (None, 30, 30, 1088) 0 block17_8_ac[0][0]
block17_9_conv[0][0]
__________________________________________________________________________________________________
block17_9_ac (Activation) (None, 30, 30, 1088) 0 block17_9[0][0]
__________________________________________________________________________________________________
conv2d_242 (Conv2D) (None, 30, 30, 128) 139264 block17_9_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_211 (BatchN (None, 30, 30, 128) 384 conv2d_242[0][0]
__________________________________________________________________________________________________
activation_211 (Activation) (None, 30, 30, 128) 0 batch_normalization_211[0][0]
__________________________________________________________________________________________________
conv2d_243 (Conv2D) (None, 30, 30, 160) 143360 activation_211[0][0]
__________________________________________________________________________________________________
batch_normalization_212 (BatchN (None, 30, 30, 160) 480 conv2d_243[0][0]
__________________________________________________________________________________________________
activation_212 (Activation) (None, 30, 30, 160) 0 batch_normalization_212[0][0]
__________________________________________________________________________________________________
conv2d_241 (Conv2D) (None, 30, 30, 192) 208896 block17_9_ac[0][0]
__________________________________________________________________________________________________
conv2d_244 (Conv2D) (None, 30, 30, 192) 215040 activation_212[0][0]
__________________________________________________________________________________________________
batch_normalization_210 (BatchN (None, 30, 30, 192) 576 conv2d_241[0][0]
__________________________________________________________________________________________________
batch_normalization_213 (BatchN (None, 30, 30, 192) 576 conv2d_244[0][0]
__________________________________________________________________________________________________
activation_210 (Activation) (None, 30, 30, 192) 0 batch_normalization_210[0][0]
__________________________________________________________________________________________________
activation_213 (Activation) (None, 30, 30, 192) 0 batch_normalization_213[0][0]
__________________________________________________________________________________________________
block17_10_mixed (Concatenate) (None, 30, 30, 384) 0 activation_210[0][0]
activation_213[0][0]
__________________________________________________________________________________________________
block17_10_conv (Conv2D) (None, 30, 30, 1088) 418880 block17_10_mixed[0][0]
__________________________________________________________________________________________________
block17_10 (Lambda) (None, 30, 30, 1088) 0 block17_9_ac[0][0]
block17_10_conv[0][0]
__________________________________________________________________________________________________
block17_10_ac (Activation) (None, 30, 30, 1088) 0 block17_10[0][0]
__________________________________________________________________________________________________
conv2d_246 (Conv2D) (None, 30, 30, 128) 139264 block17_10_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_215 (BatchN (None, 30, 30, 128) 384 conv2d_246[0][0]
__________________________________________________________________________________________________
activation_215 (Activation) (None, 30, 30, 128) 0 batch_normalization_215[0][0]
__________________________________________________________________________________________________
conv2d_247 (Conv2D) (None, 30, 30, 160) 143360 activation_215[0][0]
__________________________________________________________________________________________________
batch_normalization_216 (BatchN (None, 30, 30, 160) 480 conv2d_247[0][0]
__________________________________________________________________________________________________
activation_216 (Activation) (None, 30, 30, 160) 0 batch_normalization_216[0][0]
__________________________________________________________________________________________________
conv2d_245 (Conv2D) (None, 30, 30, 192) 208896 block17_10_ac[0][0]
__________________________________________________________________________________________________
conv2d_248 (Conv2D) (None, 30, 30, 192) 215040 activation_216[0][0]
__________________________________________________________________________________________________
batch_normalization_214 (BatchN (None, 30, 30, 192) 576 conv2d_245[0][0]
__________________________________________________________________________________________________
batch_normalization_217 (BatchN (None, 30, 30, 192) 576 conv2d_248[0][0]
__________________________________________________________________________________________________
activation_214 (Activation) (None, 30, 30, 192) 0 batch_normalization_214[0][0]
__________________________________________________________________________________________________
activation_217 (Activation) (None, 30, 30, 192) 0 batch_normalization_217[0][0]
__________________________________________________________________________________________________
block17_11_mixed (Concatenate) (None, 30, 30, 384) 0 activation_214[0][0]
activation_217[0][0]
__________________________________________________________________________________________________
block17_11_conv (Conv2D) (None, 30, 30, 1088) 418880 block17_11_mixed[0][0]
__________________________________________________________________________________________________
block17_11 (Lambda) (None, 30, 30, 1088) 0 block17_10_ac[0][0]
block17_11_conv[0][0]
__________________________________________________________________________________________________
block17_11_ac (Activation) (None, 30, 30, 1088) 0 block17_11[0][0]
__________________________________________________________________________________________________
conv2d_250 (Conv2D) (None, 30, 30, 128) 139264 block17_11_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_219 (BatchN (None, 30, 30, 128) 384 conv2d_250[0][0]
__________________________________________________________________________________________________
activation_219 (Activation) (None, 30, 30, 128) 0 batch_normalization_219[0][0]
__________________________________________________________________________________________________
conv2d_251 (Conv2D) (None, 30, 30, 160) 143360 activation_219[0][0]
__________________________________________________________________________________________________
batch_normalization_220 (BatchN (None, 30, 30, 160) 480 conv2d_251[0][0]
__________________________________________________________________________________________________
activation_220 (Activation) (None, 30, 30, 160) 0 batch_normalization_220[0][0]
__________________________________________________________________________________________________
conv2d_249 (Conv2D) (None, 30, 30, 192) 208896 block17_11_ac[0][0]
__________________________________________________________________________________________________
conv2d_252 (Conv2D) (None, 30, 30, 192) 215040 activation_220[0][0]
__________________________________________________________________________________________________
batch_normalization_218 (BatchN (None, 30, 30, 192) 576 conv2d_249[0][0]
__________________________________________________________________________________________________
batch_normalization_221 (BatchN (None, 30, 30, 192) 576 conv2d_252[0][0]
__________________________________________________________________________________________________
activation_218 (Activation) (None, 30, 30, 192) 0 batch_normalization_218[0][0]
__________________________________________________________________________________________________
activation_221 (Activation) (None, 30, 30, 192) 0 batch_normalization_221[0][0]
__________________________________________________________________________________________________
block17_12_mixed (Concatenate) (None, 30, 30, 384) 0 activation_218[0][0]
activation_221[0][0]
__________________________________________________________________________________________________
block17_12_conv (Conv2D) (None, 30, 30, 1088) 418880 block17_12_mixed[0][0]
__________________________________________________________________________________________________
block17_12 (Lambda) (None, 30, 30, 1088) 0 block17_11_ac[0][0]
block17_12_conv[0][0]
__________________________________________________________________________________________________
block17_12_ac (Activation) (None, 30, 30, 1088) 0 block17_12[0][0]
__________________________________________________________________________________________________
conv2d_254 (Conv2D) (None, 30, 30, 128) 139264 block17_12_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_223 (BatchN (None, 30, 30, 128) 384 conv2d_254[0][0]
__________________________________________________________________________________________________
activation_223 (Activation) (None, 30, 30, 128) 0 batch_normalization_223[0][0]
__________________________________________________________________________________________________
conv2d_255 (Conv2D) (None, 30, 30, 160) 143360 activation_223[0][0]
__________________________________________________________________________________________________
batch_normalization_224 (BatchN (None, 30, 30, 160) 480 conv2d_255[0][0]
__________________________________________________________________________________________________
activation_224 (Activation) (None, 30, 30, 160) 0 batch_normalization_224[0][0]
__________________________________________________________________________________________________
conv2d_253 (Conv2D) (None, 30, 30, 192) 208896 block17_12_ac[0][0]
__________________________________________________________________________________________________
conv2d_256 (Conv2D) (None, 30, 30, 192) 215040 activation_224[0][0]
__________________________________________________________________________________________________
batch_normalization_222 (BatchN (None, 30, 30, 192) 576 conv2d_253[0][0]
__________________________________________________________________________________________________
batch_normalization_225 (BatchN (None, 30, 30, 192) 576 conv2d_256[0][0]
__________________________________________________________________________________________________
activation_222 (Activation) (None, 30, 30, 192) 0 batch_normalization_222[0][0]
__________________________________________________________________________________________________
activation_225 (Activation) (None, 30, 30, 192) 0 batch_normalization_225[0][0]
__________________________________________________________________________________________________
block17_13_mixed (Concatenate) (None, 30, 30, 384) 0 activation_222[0][0]
activation_225[0][0]
__________________________________________________________________________________________________
block17_13_conv (Conv2D) (None, 30, 30, 1088) 418880 block17_13_mixed[0][0]
__________________________________________________________________________________________________
block17_13 (Lambda) (None, 30, 30, 1088) 0 block17_12_ac[0][0]
block17_13_conv[0][0]
__________________________________________________________________________________________________
block17_13_ac (Activation) (None, 30, 30, 1088) 0 block17_13[0][0]
__________________________________________________________________________________________________
conv2d_258 (Conv2D) (None, 30, 30, 128) 139264 block17_13_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_227 (BatchN (None, 30, 30, 128) 384 conv2d_258[0][0]
__________________________________________________________________________________________________
activation_227 (Activation) (None, 30, 30, 128) 0 batch_normalization_227[0][0]
__________________________________________________________________________________________________
conv2d_259 (Conv2D) (None, 30, 30, 160) 143360 activation_227[0][0]
__________________________________________________________________________________________________
batch_normalization_228 (BatchN (None, 30, 30, 160) 480 conv2d_259[0][0]
__________________________________________________________________________________________________
activation_228 (Activation) (None, 30, 30, 160) 0 batch_normalization_228[0][0]
__________________________________________________________________________________________________
conv2d_257 (Conv2D) (None, 30, 30, 192) 208896 block17_13_ac[0][0]
__________________________________________________________________________________________________
conv2d_260 (Conv2D) (None, 30, 30, 192) 215040 activation_228[0][0]
__________________________________________________________________________________________________
batch_normalization_226 (BatchN (None, 30, 30, 192) 576 conv2d_257[0][0]
__________________________________________________________________________________________________
batch_normalization_229 (BatchN (None, 30, 30, 192) 576 conv2d_260[0][0]
__________________________________________________________________________________________________
activation_226 (Activation) (None, 30, 30, 192) 0 batch_normalization_226[0][0]
__________________________________________________________________________________________________
activation_229 (Activation) (None, 30, 30, 192) 0 batch_normalization_229[0][0]
__________________________________________________________________________________________________
block17_14_mixed (Concatenate) (None, 30, 30, 384) 0 activation_226[0][0]
activation_229[0][0]
__________________________________________________________________________________________________
block17_14_conv (Conv2D) (None, 30, 30, 1088) 418880 block17_14_mixed[0][0]
__________________________________________________________________________________________________
block17_14 (Lambda) (None, 30, 30, 1088) 0 block17_13_ac[0][0]
block17_14_conv[0][0]
__________________________________________________________________________________________________
block17_14_ac (Activation) (None, 30, 30, 1088) 0 block17_14[0][0]
__________________________________________________________________________________________________
conv2d_262 (Conv2D) (None, 30, 30, 128) 139264 block17_14_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_231 (BatchN (None, 30, 30, 128) 384 conv2d_262[0][0]
__________________________________________________________________________________________________
activation_231 (Activation) (None, 30, 30, 128) 0 batch_normalization_231[0][0]
__________________________________________________________________________________________________
conv2d_263 (Conv2D) (None, 30, 30, 160) 143360 activation_231[0][0]
__________________________________________________________________________________________________
batch_normalization_232 (BatchN (None, 30, 30, 160) 480 conv2d_263[0][0]
__________________________________________________________________________________________________
activation_232 (Activation) (None, 30, 30, 160) 0 batch_normalization_232[0][0]
__________________________________________________________________________________________________
conv2d_261 (Conv2D) (None, 30, 30, 192) 208896 block17_14_ac[0][0]
__________________________________________________________________________________________________
conv2d_264 (Conv2D) (None, 30, 30, 192) 215040 activation_232[0][0]
__________________________________________________________________________________________________
batch_normalization_230 (BatchN (None, 30, 30, 192) 576 conv2d_261[0][0]
__________________________________________________________________________________________________
batch_normalization_233 (BatchN (None, 30, 30, 192) 576 conv2d_264[0][0]
__________________________________________________________________________________________________
activation_230 (Activation) (None, 30, 30, 192) 0 batch_normalization_230[0][0]
__________________________________________________________________________________________________
activation_233 (Activation) (None, 30, 30, 192) 0 batch_normalization_233[0][0]
__________________________________________________________________________________________________
block17_15_mixed (Concatenate) (None, 30, 30, 384) 0 activation_230[0][0]
activation_233[0][0]
__________________________________________________________________________________________________
block17_15_conv (Conv2D) (None, 30, 30, 1088) 418880 block17_15_mixed[0][0]
__________________________________________________________________________________________________
block17_15 (Lambda) (None, 30, 30, 1088) 0 block17_14_ac[0][0]
block17_15_conv[0][0]
__________________________________________________________________________________________________
block17_15_ac (Activation) (None, 30, 30, 1088) 0 block17_15[0][0]
__________________________________________________________________________________________________
conv2d_266 (Conv2D) (None, 30, 30, 128) 139264 block17_15_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_235 (BatchN (None, 30, 30, 128) 384 conv2d_266[0][0]
__________________________________________________________________________________________________
activation_235 (Activation) (None, 30, 30, 128) 0 batch_normalization_235[0][0]
__________________________________________________________________________________________________
conv2d_267 (Conv2D) (None, 30, 30, 160) 143360 activation_235[0][0]
__________________________________________________________________________________________________
batch_normalization_236 (BatchN (None, 30, 30, 160) 480 conv2d_267[0][0]
__________________________________________________________________________________________________
activation_236 (Activation) (None, 30, 30, 160) 0 batch_normalization_236[0][0]
__________________________________________________________________________________________________
conv2d_265 (Conv2D) (None, 30, 30, 192) 208896 block17_15_ac[0][0]
__________________________________________________________________________________________________
conv2d_268 (Conv2D) (None, 30, 30, 192) 215040 activation_236[0][0]
__________________________________________________________________________________________________
batch_normalization_234 (BatchN (None, 30, 30, 192) 576 conv2d_265[0][0]
__________________________________________________________________________________________________
batch_normalization_237 (BatchN (None, 30, 30, 192) 576 conv2d_268[0][0]
__________________________________________________________________________________________________
activation_234 (Activation) (None, 30, 30, 192) 0 batch_normalization_234[0][0]
__________________________________________________________________________________________________
activation_237 (Activation) (None, 30, 30, 192) 0 batch_normalization_237[0][0]
__________________________________________________________________________________________________
block17_16_mixed (Concatenate) (None, 30, 30, 384) 0 activation_234[0][0]
activation_237[0][0]
__________________________________________________________________________________________________
block17_16_conv (Conv2D) (None, 30, 30, 1088) 418880 block17_16_mixed[0][0]
__________________________________________________________________________________________________
block17_16 (Lambda) (None, 30, 30, 1088) 0 block17_15_ac[0][0]
block17_16_conv[0][0]
__________________________________________________________________________________________________
block17_16_ac (Activation) (None, 30, 30, 1088) 0 block17_16[0][0]
__________________________________________________________________________________________________
conv2d_270 (Conv2D) (None, 30, 30, 128) 139264 block17_16_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_239 (BatchN (None, 30, 30, 128) 384 conv2d_270[0][0]
__________________________________________________________________________________________________
activation_239 (Activation) (None, 30, 30, 128) 0 batch_normalization_239[0][0]
__________________________________________________________________________________________________
conv2d_271 (Conv2D) (None, 30, 30, 160) 143360 activation_239[0][0]
__________________________________________________________________________________________________
batch_normalization_240 (BatchN (None, 30, 30, 160) 480 conv2d_271[0][0]
__________________________________________________________________________________________________
activation_240 (Activation) (None, 30, 30, 160) 0 batch_normalization_240[0][0]
__________________________________________________________________________________________________
conv2d_269 (Conv2D) (None, 30, 30, 192) 208896 block17_16_ac[0][0]
__________________________________________________________________________________________________
conv2d_272 (Conv2D) (None, 30, 30, 192) 215040 activation_240[0][0]
__________________________________________________________________________________________________
batch_normalization_238 (BatchN (None, 30, 30, 192) 576 conv2d_269[0][0]
__________________________________________________________________________________________________
batch_normalization_241 (BatchN (None, 30, 30, 192) 576 conv2d_272[0][0]
__________________________________________________________________________________________________
activation_238 (Activation) (None, 30, 30, 192) 0 batch_normalization_238[0][0]
__________________________________________________________________________________________________
activation_241 (Activation) (None, 30, 30, 192) 0 batch_normalization_241[0][0]
__________________________________________________________________________________________________
block17_17_mixed (Concatenate) (None, 30, 30, 384) 0 activation_238[0][0]
activation_241[0][0]
__________________________________________________________________________________________________
block17_17_conv (Conv2D) (None, 30, 30, 1088) 418880 block17_17_mixed[0][0]
__________________________________________________________________________________________________
block17_17 (Lambda) (None, 30, 30, 1088) 0 block17_16_ac[0][0]
block17_17_conv[0][0]
__________________________________________________________________________________________________
block17_17_ac (Activation) (None, 30, 30, 1088) 0 block17_17[0][0]
__________________________________________________________________________________________________
conv2d_274 (Conv2D) (None, 30, 30, 128) 139264 block17_17_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_243 (BatchN (None, 30, 30, 128) 384 conv2d_274[0][0]
__________________________________________________________________________________________________
activation_243 (Activation) (None, 30, 30, 128) 0 batch_normalization_243[0][0]
__________________________________________________________________________________________________
conv2d_275 (Conv2D) (None, 30, 30, 160) 143360 activation_243[0][0]
__________________________________________________________________________________________________
batch_normalization_244 (BatchN (None, 30, 30, 160) 480 conv2d_275[0][0]
__________________________________________________________________________________________________
activation_244 (Activation) (None, 30, 30, 160) 0 batch_normalization_244[0][0]
__________________________________________________________________________________________________
conv2d_273 (Conv2D) (None, 30, 30, 192) 208896 block17_17_ac[0][0]
__________________________________________________________________________________________________
conv2d_276 (Conv2D) (None, 30, 30, 192) 215040 activation_244[0][0]
__________________________________________________________________________________________________
batch_normalization_242 (BatchN (None, 30, 30, 192) 576 conv2d_273[0][0]
__________________________________________________________________________________________________
batch_normalization_245 (BatchN (None, 30, 30, 192) 576 conv2d_276[0][0]
__________________________________________________________________________________________________
activation_242 (Activation) (None, 30, 30, 192) 0 batch_normalization_242[0][0]
__________________________________________________________________________________________________
activation_245 (Activation) (None, 30, 30, 192) 0 batch_normalization_245[0][0]
__________________________________________________________________________________________________
block17_18_mixed (Concatenate) (None, 30, 30, 384) 0 activation_242[0][0]
activation_245[0][0]
__________________________________________________________________________________________________
block17_18_conv (Conv2D) (None, 30, 30, 1088) 418880 block17_18_mixed[0][0]
__________________________________________________________________________________________________
block17_18 (Lambda) (None, 30, 30, 1088) 0 block17_17_ac[0][0]
block17_18_conv[0][0]
__________________________________________________________________________________________________
block17_18_ac (Activation) (None, 30, 30, 1088) 0 block17_18[0][0]
__________________________________________________________________________________________________
conv2d_278 (Conv2D) (None, 30, 30, 128) 139264 block17_18_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_247 (BatchN (None, 30, 30, 128) 384 conv2d_278[0][0]
__________________________________________________________________________________________________
activation_247 (Activation) (None, 30, 30, 128) 0 batch_normalization_247[0][0]
__________________________________________________________________________________________________
conv2d_279 (Conv2D) (None, 30, 30, 160) 143360 activation_247[0][0]
__________________________________________________________________________________________________
batch_normalization_248 (BatchN (None, 30, 30, 160) 480 conv2d_279[0][0]
__________________________________________________________________________________________________
activation_248 (Activation) (None, 30, 30, 160) 0 batch_normalization_248[0][0]
__________________________________________________________________________________________________
conv2d_277 (Conv2D) (None, 30, 30, 192) 208896 block17_18_ac[0][0]
__________________________________________________________________________________________________
conv2d_280 (Conv2D) (None, 30, 30, 192) 215040 activation_248[0][0]
__________________________________________________________________________________________________
batch_normalization_246 (BatchN (None, 30, 30, 192) 576 conv2d_277[0][0]
__________________________________________________________________________________________________
batch_normalization_249 (BatchN (None, 30, 30, 192) 576 conv2d_280[0][0]
__________________________________________________________________________________________________
activation_246 (Activation) (None, 30, 30, 192) 0 batch_normalization_246[0][0]
__________________________________________________________________________________________________
activation_249 (Activation) (None, 30, 30, 192) 0 batch_normalization_249[0][0]
__________________________________________________________________________________________________
block17_19_mixed (Concatenate) (None, 30, 30, 384) 0 activation_246[0][0]
activation_249[0][0]
__________________________________________________________________________________________________
block17_19_conv (Conv2D) (None, 30, 30, 1088) 418880 block17_19_mixed[0][0]
__________________________________________________________________________________________________
block17_19 (Lambda) (None, 30, 30, 1088) 0 block17_18_ac[0][0]
block17_19_conv[0][0]
__________________________________________________________________________________________________
block17_19_ac (Activation) (None, 30, 30, 1088) 0 block17_19[0][0]
__________________________________________________________________________________________________
conv2d_282 (Conv2D) (None, 30, 30, 128) 139264 block17_19_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_251 (BatchN (None, 30, 30, 128) 384 conv2d_282[0][0]
__________________________________________________________________________________________________
activation_251 (Activation) (None, 30, 30, 128) 0 batch_normalization_251[0][0]
__________________________________________________________________________________________________
conv2d_283 (Conv2D) (None, 30, 30, 160) 143360 activation_251[0][0]
__________________________________________________________________________________________________
batch_normalization_252 (BatchN (None, 30, 30, 160) 480 conv2d_283[0][0]
__________________________________________________________________________________________________
activation_252 (Activation) (None, 30, 30, 160) 0 batch_normalization_252[0][0]
__________________________________________________________________________________________________
conv2d_281 (Conv2D) (None, 30, 30, 192) 208896 block17_19_ac[0][0]
__________________________________________________________________________________________________
conv2d_284 (Conv2D) (None, 30, 30, 192) 215040 activation_252[0][0]
__________________________________________________________________________________________________
batch_normalization_250 (BatchN (None, 30, 30, 192) 576 conv2d_281[0][0]
__________________________________________________________________________________________________
batch_normalization_253 (BatchN (None, 30, 30, 192) 576 conv2d_284[0][0]
__________________________________________________________________________________________________
activation_250 (Activation) (None, 30, 30, 192) 0 batch_normalization_250[0][0]
__________________________________________________________________________________________________
activation_253 (Activation) (None, 30, 30, 192) 0 batch_normalization_253[0][0]
__________________________________________________________________________________________________
block17_20_mixed (Concatenate) (None, 30, 30, 384) 0 activation_250[0][0]
activation_253[0][0]
__________________________________________________________________________________________________
block17_20_conv (Conv2D) (None, 30, 30, 1088) 418880 block17_20_mixed[0][0]
__________________________________________________________________________________________________
block17_20 (Lambda) (None, 30, 30, 1088) 0 block17_19_ac[0][0]
block17_20_conv[0][0]
__________________________________________________________________________________________________
block17_20_ac (Activation) (None, 30, 30, 1088) 0 block17_20[0][0]
__________________________________________________________________________________________________
conv2d_289 (Conv2D) (None, 30, 30, 256) 278528 block17_20_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_258 (BatchN (None, 30, 30, 256) 768 conv2d_289[0][0]
__________________________________________________________________________________________________
activation_258 (Activation) (None, 30, 30, 256) 0 batch_normalization_258[0][0]
__________________________________________________________________________________________________
conv2d_285 (Conv2D) (None, 30, 30, 256) 278528 block17_20_ac[0][0]
__________________________________________________________________________________________________
conv2d_287 (Conv2D) (None, 30, 30, 256) 278528 block17_20_ac[0][0]
__________________________________________________________________________________________________
conv2d_290 (Conv2D) (None, 30, 30, 288) 663552 activation_258[0][0]
__________________________________________________________________________________________________
batch_normalization_254 (BatchN (None, 30, 30, 256) 768 conv2d_285[0][0]
__________________________________________________________________________________________________
batch_normalization_256 (BatchN (None, 30, 30, 256) 768 conv2d_287[0][0]
__________________________________________________________________________________________________
batch_normalization_259 (BatchN (None, 30, 30, 288) 864 conv2d_290[0][0]
__________________________________________________________________________________________________
activation_254 (Activation) (None, 30, 30, 256) 0 batch_normalization_254[0][0]
__________________________________________________________________________________________________
activation_256 (Activation) (None, 30, 30, 256) 0 batch_normalization_256[0][0]
__________________________________________________________________________________________________
activation_259 (Activation) (None, 30, 30, 288) 0 batch_normalization_259[0][0]
__________________________________________________________________________________________________
conv2d_286 (Conv2D) (None, 14, 14, 384) 884736 activation_254[0][0]
__________________________________________________________________________________________________
conv2d_288 (Conv2D) (None, 14, 14, 288) 663552 activation_256[0][0]
__________________________________________________________________________________________________
conv2d_291 (Conv2D) (None, 14, 14, 320) 829440 activation_259[0][0]
__________________________________________________________________________________________________
batch_normalization_255 (BatchN (None, 14, 14, 384) 1152 conv2d_286[0][0]
__________________________________________________________________________________________________
batch_normalization_257 (BatchN (None, 14, 14, 288) 864 conv2d_288[0][0]
__________________________________________________________________________________________________
batch_normalization_260 (BatchN (None, 14, 14, 320) 960 conv2d_291[0][0]
__________________________________________________________________________________________________
activation_255 (Activation) (None, 14, 14, 384) 0 batch_normalization_255[0][0]
__________________________________________________________________________________________________
activation_257 (Activation) (None, 14, 14, 288) 0 batch_normalization_257[0][0]
__________________________________________________________________________________________________
activation_260 (Activation) (None, 14, 14, 320) 0 batch_normalization_260[0][0]
__________________________________________________________________________________________________
max_pooling2d_17 (MaxPooling2D) (None, 14, 14, 1088) 0 block17_20_ac[0][0]
__________________________________________________________________________________________________
mixed_7a (Concatenate) (None, 14, 14, 2080) 0 activation_255[0][0]
activation_257[0][0]
activation_260[0][0]
max_pooling2d_17[0][0]
__________________________________________________________________________________________________
conv2d_293 (Conv2D) (None, 14, 14, 192) 399360 mixed_7a[0][0]
__________________________________________________________________________________________________
batch_normalization_262 (BatchN (None, 14, 14, 192) 576 conv2d_293[0][0]
__________________________________________________________________________________________________
activation_262 (Activation) (None, 14, 14, 192) 0 batch_normalization_262[0][0]
__________________________________________________________________________________________________
conv2d_294 (Conv2D) (None, 14, 14, 224) 129024 activation_262[0][0]
__________________________________________________________________________________________________
batch_normalization_263 (BatchN (None, 14, 14, 224) 672 conv2d_294[0][0]
__________________________________________________________________________________________________
activation_263 (Activation) (None, 14, 14, 224) 0 batch_normalization_263[0][0]
__________________________________________________________________________________________________
conv2d_292 (Conv2D) (None, 14, 14, 192) 399360 mixed_7a[0][0]
__________________________________________________________________________________________________
conv2d_295 (Conv2D) (None, 14, 14, 256) 172032 activation_263[0][0]
__________________________________________________________________________________________________
batch_normalization_261 (BatchN (None, 14, 14, 192) 576 conv2d_292[0][0]
__________________________________________________________________________________________________
batch_normalization_264 (BatchN (None, 14, 14, 256) 768 conv2d_295[0][0]
__________________________________________________________________________________________________
activation_261 (Activation) (None, 14, 14, 192) 0 batch_normalization_261[0][0]
__________________________________________________________________________________________________
activation_264 (Activation) (None, 14, 14, 256) 0 batch_normalization_264[0][0]
__________________________________________________________________________________________________
block8_1_mixed (Concatenate) (None, 14, 14, 448) 0 activation_261[0][0]
activation_264[0][0]
__________________________________________________________________________________________________
block8_1_conv (Conv2D) (None, 14, 14, 2080) 933920 block8_1_mixed[0][0]
__________________________________________________________________________________________________
block8_1 (Lambda) (None, 14, 14, 2080) 0 mixed_7a[0][0]
block8_1_conv[0][0]
__________________________________________________________________________________________________
block8_1_ac (Activation) (None, 14, 14, 2080) 0 block8_1[0][0]
__________________________________________________________________________________________________
conv2d_297 (Conv2D) (None, 14, 14, 192) 399360 block8_1_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_266 (BatchN (None, 14, 14, 192) 576 conv2d_297[0][0]
__________________________________________________________________________________________________
activation_266 (Activation) (None, 14, 14, 192) 0 batch_normalization_266[0][0]
__________________________________________________________________________________________________
conv2d_298 (Conv2D) (None, 14, 14, 224) 129024 activation_266[0][0]
__________________________________________________________________________________________________
batch_normalization_267 (BatchN (None, 14, 14, 224) 672 conv2d_298[0][0]
__________________________________________________________________________________________________
activation_267 (Activation) (None, 14, 14, 224) 0 batch_normalization_267[0][0]
__________________________________________________________________________________________________
conv2d_296 (Conv2D) (None, 14, 14, 192) 399360 block8_1_ac[0][0]
__________________________________________________________________________________________________
conv2d_299 (Conv2D) (None, 14, 14, 256) 172032 activation_267[0][0]
__________________________________________________________________________________________________
batch_normalization_265 (BatchN (None, 14, 14, 192) 576 conv2d_296[0][0]
__________________________________________________________________________________________________
batch_normalization_268 (BatchN (None, 14, 14, 256) 768 conv2d_299[0][0]
__________________________________________________________________________________________________
activation_265 (Activation) (None, 14, 14, 192) 0 batch_normalization_265[0][0]
__________________________________________________________________________________________________
activation_268 (Activation) (None, 14, 14, 256) 0 batch_normalization_268[0][0]
__________________________________________________________________________________________________
block8_2_mixed (Concatenate) (None, 14, 14, 448) 0 activation_265[0][0]
activation_268[0][0]
__________________________________________________________________________________________________
block8_2_conv (Conv2D) (None, 14, 14, 2080) 933920 block8_2_mixed[0][0]
__________________________________________________________________________________________________
block8_2 (Lambda) (None, 14, 14, 2080) 0 block8_1_ac[0][0]
block8_2_conv[0][0]
__________________________________________________________________________________________________
block8_2_ac (Activation) (None, 14, 14, 2080) 0 block8_2[0][0]
__________________________________________________________________________________________________
conv2d_301 (Conv2D) (None, 14, 14, 192) 399360 block8_2_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_270 (BatchN (None, 14, 14, 192) 576 conv2d_301[0][0]
__________________________________________________________________________________________________
activation_270 (Activation) (None, 14, 14, 192) 0 batch_normalization_270[0][0]
__________________________________________________________________________________________________
conv2d_302 (Conv2D) (None, 14, 14, 224) 129024 activation_270[0][0]
__________________________________________________________________________________________________
batch_normalization_271 (BatchN (None, 14, 14, 224) 672 conv2d_302[0][0]
__________________________________________________________________________________________________
activation_271 (Activation) (None, 14, 14, 224) 0 batch_normalization_271[0][0]
__________________________________________________________________________________________________
conv2d_300 (Conv2D) (None, 14, 14, 192) 399360 block8_2_ac[0][0]
__________________________________________________________________________________________________
conv2d_303 (Conv2D) (None, 14, 14, 256) 172032 activation_271[0][0]
__________________________________________________________________________________________________
batch_normalization_269 (BatchN (None, 14, 14, 192) 576 conv2d_300[0][0]
__________________________________________________________________________________________________
batch_normalization_272 (BatchN (None, 14, 14, 256) 768 conv2d_303[0][0]
__________________________________________________________________________________________________
activation_269 (Activation) (None, 14, 14, 192) 0 batch_normalization_269[0][0]
__________________________________________________________________________________________________
activation_272 (Activation) (None, 14, 14, 256) 0 batch_normalization_272[0][0]
__________________________________________________________________________________________________
block8_3_mixed (Concatenate) (None, 14, 14, 448) 0 activation_269[0][0]
activation_272[0][0]
__________________________________________________________________________________________________
block8_3_conv (Conv2D) (None, 14, 14, 2080) 933920 block8_3_mixed[0][0]
__________________________________________________________________________________________________
block8_3 (Lambda) (None, 14, 14, 2080) 0 block8_2_ac[0][0]
block8_3_conv[0][0]
__________________________________________________________________________________________________
block8_3_ac (Activation) (None, 14, 14, 2080) 0 block8_3[0][0]
__________________________________________________________________________________________________
conv2d_305 (Conv2D) (None, 14, 14, 192) 399360 block8_3_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_274 (BatchN (None, 14, 14, 192) 576 conv2d_305[0][0]
__________________________________________________________________________________________________
activation_274 (Activation) (None, 14, 14, 192) 0 batch_normalization_274[0][0]
__________________________________________________________________________________________________
conv2d_306 (Conv2D) (None, 14, 14, 224) 129024 activation_274[0][0]
__________________________________________________________________________________________________
batch_normalization_275 (BatchN (None, 14, 14, 224) 672 conv2d_306[0][0]
__________________________________________________________________________________________________
activation_275 (Activation) (None, 14, 14, 224) 0 batch_normalization_275[0][0]
__________________________________________________________________________________________________
conv2d_304 (Conv2D) (None, 14, 14, 192) 399360 block8_3_ac[0][0]
__________________________________________________________________________________________________
conv2d_307 (Conv2D) (None, 14, 14, 256) 172032 activation_275[0][0]
__________________________________________________________________________________________________
batch_normalization_273 (BatchN (None, 14, 14, 192) 576 conv2d_304[0][0]
__________________________________________________________________________________________________
batch_normalization_276 (BatchN (None, 14, 14, 256) 768 conv2d_307[0][0]
__________________________________________________________________________________________________
activation_273 (Activation) (None, 14, 14, 192) 0 batch_normalization_273[0][0]
__________________________________________________________________________________________________
activation_276 (Activation) (None, 14, 14, 256) 0 batch_normalization_276[0][0]
__________________________________________________________________________________________________
block8_4_mixed (Concatenate) (None, 14, 14, 448) 0 activation_273[0][0]
activation_276[0][0]
__________________________________________________________________________________________________
block8_4_conv (Conv2D) (None, 14, 14, 2080) 933920 block8_4_mixed[0][0]
__________________________________________________________________________________________________
block8_4 (Lambda) (None, 14, 14, 2080) 0 block8_3_ac[0][0]
block8_4_conv[0][0]
__________________________________________________________________________________________________
block8_4_ac (Activation) (None, 14, 14, 2080) 0 block8_4[0][0]
__________________________________________________________________________________________________
conv2d_309 (Conv2D) (None, 14, 14, 192) 399360 block8_4_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_278 (BatchN (None, 14, 14, 192) 576 conv2d_309[0][0]
__________________________________________________________________________________________________
activation_278 (Activation) (None, 14, 14, 192) 0 batch_normalization_278[0][0]
__________________________________________________________________________________________________
conv2d_310 (Conv2D) (None, 14, 14, 224) 129024 activation_278[0][0]
__________________________________________________________________________________________________
batch_normalization_279 (BatchN (None, 14, 14, 224) 672 conv2d_310[0][0]
__________________________________________________________________________________________________
activation_279 (Activation) (None, 14, 14, 224) 0 batch_normalization_279[0][0]
__________________________________________________________________________________________________
conv2d_308 (Conv2D) (None, 14, 14, 192) 399360 block8_4_ac[0][0]
__________________________________________________________________________________________________
conv2d_311 (Conv2D) (None, 14, 14, 256) 172032 activation_279[0][0]
__________________________________________________________________________________________________
batch_normalization_277 (BatchN (None, 14, 14, 192) 576 conv2d_308[0][0]
__________________________________________________________________________________________________
batch_normalization_280 (BatchN (None, 14, 14, 256) 768 conv2d_311[0][0]
__________________________________________________________________________________________________
activation_277 (Activation) (None, 14, 14, 192) 0 batch_normalization_277[0][0]
__________________________________________________________________________________________________
activation_280 (Activation) (None, 14, 14, 256) 0 batch_normalization_280[0][0]
__________________________________________________________________________________________________
block8_5_mixed (Concatenate) (None, 14, 14, 448) 0 activation_277[0][0]
activation_280[0][0]
__________________________________________________________________________________________________
block8_5_conv (Conv2D) (None, 14, 14, 2080) 933920 block8_5_mixed[0][0]
__________________________________________________________________________________________________
block8_5 (Lambda) (None, 14, 14, 2080) 0 block8_4_ac[0][0]
block8_5_conv[0][0]
__________________________________________________________________________________________________
block8_5_ac (Activation) (None, 14, 14, 2080) 0 block8_5[0][0]
__________________________________________________________________________________________________
conv2d_313 (Conv2D) (None, 14, 14, 192) 399360 block8_5_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_282 (BatchN (None, 14, 14, 192) 576 conv2d_313[0][0]
__________________________________________________________________________________________________
activation_282 (Activation) (None, 14, 14, 192) 0 batch_normalization_282[0][0]
__________________________________________________________________________________________________
conv2d_314 (Conv2D) (None, 14, 14, 224) 129024 activation_282[0][0]
__________________________________________________________________________________________________
batch_normalization_283 (BatchN (None, 14, 14, 224) 672 conv2d_314[0][0]
__________________________________________________________________________________________________
activation_283 (Activation) (None, 14, 14, 224) 0 batch_normalization_283[0][0]
__________________________________________________________________________________________________
conv2d_312 (Conv2D) (None, 14, 14, 192) 399360 block8_5_ac[0][0]
__________________________________________________________________________________________________
conv2d_315 (Conv2D) (None, 14, 14, 256) 172032 activation_283[0][0]
__________________________________________________________________________________________________
batch_normalization_281 (BatchN (None, 14, 14, 192) 576 conv2d_312[0][0]
__________________________________________________________________________________________________
batch_normalization_284 (BatchN (None, 14, 14, 256) 768 conv2d_315[0][0]
__________________________________________________________________________________________________
activation_281 (Activation) (None, 14, 14, 192) 0 batch_normalization_281[0][0]
__________________________________________________________________________________________________
activation_284 (Activation) (None, 14, 14, 256) 0 batch_normalization_284[0][0]
__________________________________________________________________________________________________
block8_6_mixed (Concatenate) (None, 14, 14, 448) 0 activation_281[0][0]
activation_284[0][0]
__________________________________________________________________________________________________
block8_6_conv (Conv2D) (None, 14, 14, 2080) 933920 block8_6_mixed[0][0]
__________________________________________________________________________________________________
block8_6 (Lambda) (None, 14, 14, 2080) 0 block8_5_ac[0][0]
block8_6_conv[0][0]
__________________________________________________________________________________________________
block8_6_ac (Activation) (None, 14, 14, 2080) 0 block8_6[0][0]
__________________________________________________________________________________________________
conv2d_317 (Conv2D) (None, 14, 14, 192) 399360 block8_6_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_286 (BatchN (None, 14, 14, 192) 576 conv2d_317[0][0]
__________________________________________________________________________________________________
activation_286 (Activation) (None, 14, 14, 192) 0 batch_normalization_286[0][0]
__________________________________________________________________________________________________
conv2d_318 (Conv2D) (None, 14, 14, 224) 129024 activation_286[0][0]
__________________________________________________________________________________________________
batch_normalization_287 (BatchN (None, 14, 14, 224) 672 conv2d_318[0][0]
__________________________________________________________________________________________________
activation_287 (Activation) (None, 14, 14, 224) 0 batch_normalization_287[0][0]
__________________________________________________________________________________________________
conv2d_316 (Conv2D) (None, 14, 14, 192) 399360 block8_6_ac[0][0]
__________________________________________________________________________________________________
conv2d_319 (Conv2D) (None, 14, 14, 256) 172032 activation_287[0][0]
__________________________________________________________________________________________________
batch_normalization_285 (BatchN (None, 14, 14, 192) 576 conv2d_316[0][0]
__________________________________________________________________________________________________
batch_normalization_288 (BatchN (None, 14, 14, 256) 768 conv2d_319[0][0]
__________________________________________________________________________________________________
activation_285 (Activation) (None, 14, 14, 192) 0 batch_normalization_285[0][0]
__________________________________________________________________________________________________
activation_288 (Activation) (None, 14, 14, 256) 0 batch_normalization_288[0][0]
__________________________________________________________________________________________________
block8_7_mixed (Concatenate) (None, 14, 14, 448) 0 activation_285[0][0]
activation_288[0][0]
__________________________________________________________________________________________________
block8_7_conv (Conv2D) (None, 14, 14, 2080) 933920 block8_7_mixed[0][0]
__________________________________________________________________________________________________
block8_7 (Lambda) (None, 14, 14, 2080) 0 block8_6_ac[0][0]
block8_7_conv[0][0]
__________________________________________________________________________________________________
block8_7_ac (Activation) (None, 14, 14, 2080) 0 block8_7[0][0]
__________________________________________________________________________________________________
conv2d_321 (Conv2D) (None, 14, 14, 192) 399360 block8_7_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_290 (BatchN (None, 14, 14, 192) 576 conv2d_321[0][0]
__________________________________________________________________________________________________
activation_290 (Activation) (None, 14, 14, 192) 0 batch_normalization_290[0][0]
__________________________________________________________________________________________________
conv2d_322 (Conv2D) (None, 14, 14, 224) 129024 activation_290[0][0]
__________________________________________________________________________________________________
batch_normalization_291 (BatchN (None, 14, 14, 224) 672 conv2d_322[0][0]
__________________________________________________________________________________________________
activation_291 (Activation) (None, 14, 14, 224) 0 batch_normalization_291[0][0]
__________________________________________________________________________________________________
conv2d_320 (Conv2D) (None, 14, 14, 192) 399360 block8_7_ac[0][0]
__________________________________________________________________________________________________
conv2d_323 (Conv2D) (None, 14, 14, 256) 172032 activation_291[0][0]
__________________________________________________________________________________________________
batch_normalization_289 (BatchN (None, 14, 14, 192) 576 conv2d_320[0][0]
__________________________________________________________________________________________________
batch_normalization_292 (BatchN (None, 14, 14, 256) 768 conv2d_323[0][0]
__________________________________________________________________________________________________
activation_289 (Activation) (None, 14, 14, 192) 0 batch_normalization_289[0][0]
__________________________________________________________________________________________________
activation_292 (Activation) (None, 14, 14, 256) 0 batch_normalization_292[0][0]
__________________________________________________________________________________________________
block8_8_mixed (Concatenate) (None, 14, 14, 448) 0 activation_289[0][0]
activation_292[0][0]
__________________________________________________________________________________________________
block8_8_conv (Conv2D) (None, 14, 14, 2080) 933920 block8_8_mixed[0][0]
__________________________________________________________________________________________________
block8_8 (Lambda) (None, 14, 14, 2080) 0 block8_7_ac[0][0]
block8_8_conv[0][0]
__________________________________________________________________________________________________
block8_8_ac (Activation) (None, 14, 14, 2080) 0 block8_8[0][0]
__________________________________________________________________________________________________
conv2d_325 (Conv2D) (None, 14, 14, 192) 399360 block8_8_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_294 (BatchN (None, 14, 14, 192) 576 conv2d_325[0][0]
__________________________________________________________________________________________________
activation_294 (Activation) (None, 14, 14, 192) 0 batch_normalization_294[0][0]
__________________________________________________________________________________________________
conv2d_326 (Conv2D) (None, 14, 14, 224) 129024 activation_294[0][0]
__________________________________________________________________________________________________
batch_normalization_295 (BatchN (None, 14, 14, 224) 672 conv2d_326[0][0]
__________________________________________________________________________________________________
activation_295 (Activation) (None, 14, 14, 224) 0 batch_normalization_295[0][0]
__________________________________________________________________________________________________
conv2d_324 (Conv2D) (None, 14, 14, 192) 399360 block8_8_ac[0][0]
__________________________________________________________________________________________________
conv2d_327 (Conv2D) (None, 14, 14, 256) 172032 activation_295[0][0]
__________________________________________________________________________________________________
batch_normalization_293 (BatchN (None, 14, 14, 192) 576 conv2d_324[0][0]
__________________________________________________________________________________________________
batch_normalization_296 (BatchN (None, 14, 14, 256) 768 conv2d_327[0][0]
__________________________________________________________________________________________________
activation_293 (Activation) (None, 14, 14, 192) 0 batch_normalization_293[0][0]
__________________________________________________________________________________________________
activation_296 (Activation) (None, 14, 14, 256) 0 batch_normalization_296[0][0]
__________________________________________________________________________________________________
block8_9_mixed (Concatenate) (None, 14, 14, 448) 0 activation_293[0][0]
activation_296[0][0]
__________________________________________________________________________________________________
block8_9_conv (Conv2D) (None, 14, 14, 2080) 933920 block8_9_mixed[0][0]
__________________________________________________________________________________________________
block8_9 (Lambda) (None, 14, 14, 2080) 0 block8_8_ac[0][0]
block8_9_conv[0][0]
__________________________________________________________________________________________________
block8_9_ac (Activation) (None, 14, 14, 2080) 0 block8_9[0][0]
__________________________________________________________________________________________________
conv2d_329 (Conv2D) (None, 14, 14, 192) 399360 block8_9_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_298 (BatchN (None, 14, 14, 192) 576 conv2d_329[0][0]
__________________________________________________________________________________________________
activation_298 (Activation) (None, 14, 14, 192) 0 batch_normalization_298[0][0]
__________________________________________________________________________________________________
conv2d_330 (Conv2D) (None, 14, 14, 224) 129024 activation_298[0][0]
__________________________________________________________________________________________________
batch_normalization_299 (BatchN (None, 14, 14, 224) 672 conv2d_330[0][0]
__________________________________________________________________________________________________
activation_299 (Activation) (None, 14, 14, 224) 0 batch_normalization_299[0][0]
__________________________________________________________________________________________________
conv2d_328 (Conv2D) (None, 14, 14, 192) 399360 block8_9_ac[0][0]
__________________________________________________________________________________________________
conv2d_331 (Conv2D) (None, 14, 14, 256) 172032 activation_299[0][0]
__________________________________________________________________________________________________
batch_normalization_297 (BatchN (None, 14, 14, 192) 576 conv2d_328[0][0]
__________________________________________________________________________________________________
batch_normalization_300 (BatchN (None, 14, 14, 256) 768 conv2d_331[0][0]
__________________________________________________________________________________________________
activation_297 (Activation) (None, 14, 14, 192) 0 batch_normalization_297[0][0]
__________________________________________________________________________________________________
activation_300 (Activation) (None, 14, 14, 256) 0 batch_normalization_300[0][0]
__________________________________________________________________________________________________
block8_10_mixed (Concatenate) (None, 14, 14, 448) 0 activation_297[0][0]
activation_300[0][0]
__________________________________________________________________________________________________
block8_10_conv (Conv2D) (None, 14, 14, 2080) 933920 block8_10_mixed[0][0]
__________________________________________________________________________________________________
block8_10 (Lambda) (None, 14, 14, 2080) 0 block8_9_ac[0][0]
block8_10_conv[0][0]
__________________________________________________________________________________________________
conv_7b (Conv2D) (None, 14, 14, 1536) 3194880 block8_10[0][0]
__________________________________________________________________________________________________
conv_7b_bn (BatchNormalization) (None, 14, 14, 1536) 4608 conv_7b[0][0]
__________________________________________________________________________________________________
conv_7b_ac (Activation) (None, 14, 14, 1536) 0 conv_7b_bn[0][0]
__________________________________________________________________________________________________
global_average_pooling2d_3 (Glo (None, 1536) 0 conv_7b_ac[0][0]
__________________________________________________________________________________________________
dense_12 (Dense) (None, 516) 793092 global_average_pooling2d_3[0][0]
__________________________________________________________________________________________________
dropout_6 (Dropout) (None, 516) 0 dense_12[0][0]
__________________________________________________________________________________________________
dense_13 (Dense) (None, 256) 132352 dropout_6[0][0]
__________________________________________________________________________________________________
dropout_7 (Dropout) (None, 256) 0 dense_13[0][0]
__________________________________________________________________________________________________
dense_14 (Dense) (None, 64) 16448 dropout_7[0][0]
__________________________________________________________________________________________________
dense_15 (Dense) (None, 3) 195 dense_14[0][0]
==================================================================================================
Total params: 55,278,823
Trainable params: 13,383,143
Non-trainable params: 41,895,680
__________________________________________________________________________________________________
None
In [64]:
# Define modifier to replace the sigmoid function of the last layer to a linear function
def model_modifier(m):
m.layers[-1].activation = tf.keras.activations.linear
# Define losses functions. 0 is the index for a normal MRI
loss_normal = lambda output: K.mean(output[:, 0])
# Define losses functions. 1 is the index for a diffuse malformation of cortical development MRI
loss_diffuseMCD = lambda output: K.mean(output[:, 1])
# Define losses functions. 2 is the index for a PVNH MRI
loss_PVNH = lambda output: K.mean(output[:, 2])
# Create Gradcam object
gradcam = Gradcam(model, model_modifier)
# Create Saliency object
saliency = Saliency(model, model_modifier)
# Iterate through the MRIs in test set
# Set background to white color
plt.rcParams['axes.facecolor']='white'
plt.rcParams['figure.facecolor']='white'
plt.rcParams['figure.edgecolor']='white'
print('\n \n' + '\033[1m' + 'EACH ORIGINAL MRI IS ANALYZED WITH TWO METHODS: CLASS ACTIVATION MAP (UPPER ROW) AND SALIENCY MAP (LOWER ROW)' + '\033[0m' + '\n')
print('\033[1m' + 'EACH MAP IS SUPERIMPOSED ON THE ORIGINAL MRI WITH A TRANSPARENCY THAT IS INVERSELY PROPORTIONAL TO THE ESTIMATED PROBABILITY OF THE MRI BELONGING TO THAT CATEGORY (NORMAL MRI, DIFFUSE CORTICAL MALFORMATION, OR PERIVENTRICULAR NODULAR HETEROTOPIA) \n \nHIGHER ESTIMATED PROBABILITIES PRODUCE CLEARLY SEEN MAPS OVERLAID ON THE ORIGINAL MRI AND LOWER ESTIMATED PROBABILITIES PRODUCE VERY TRANSPARENT OR NOT APPRECIABLE MAPS OVERLAID ON THE ORIGINAL MRI' + '\033[0m'+ '\n')
for i in range(20):
# Print spaces to separate from the next image
print('\n \n \n \n \n \n')
# Print real classification of the image
if y_true[i]==0:
real_classification='Normal MRI'
elif y_true[i]==1:
real_classification='Diffuse MCD'
else:
real_classification='PVNH'
print('\033[1m' + 'REAL CLASSIFICATION OF THE IMAGE: {}'.format(real_classification) + '\033[0m')
# Print model classification and model probability of MCD
if y_predInceptionResNetV2[i]==0:
predicted_classification='Normal MRI'
elif y_predInceptionResNetV2[i]==1:
predicted_classification='Diffuse MCD'
else:
predicted_classification='PVNH'
print('\033[1m' + 'MODEL CLASSIFICATION OF THE IMAGE: {}'.format(predicted_classification) + '\033[0m \n')
print('\033[1m' + ' Prob. Normal MRI: {:.4f} '.format(valInceptionResNetV2[i][0]) + 'Prob. Diffuse MCD: {:.4f} '.format(valInceptionResNetV2[i][1]), 'Prob. PVNH: {:.4f}'.format(valInceptionResNetV2[i][2]) + '\033[0m')
# Arrays to plot
original_image=shuffled_val_X[i]
list_heatmaps=[
# GradCam heatmap for normal MRI
normalize(gradcam(loss_normal, shuffled_val_X[i])),
# GradCam heatmap for diffuse MCD
normalize(gradcam(loss_diffuseMCD, shuffled_val_X[i])),
# GradCam heatmap for PVNH
normalize(gradcam(loss_PVNH, shuffled_val_X[i])),
# Saliency heatmap for normal MRI
normalize(saliency(loss_normal, seed_input=np.expand_dims(shuffled_val_X[i], axis=0), smooth_noise=0.2)),
# Saliency heatmap for diffuse MCD
normalize(saliency(loss_diffuseMCD, seed_input=np.expand_dims(shuffled_val_X[i], axis=0), smooth_noise=0.2)),
# Saliency heatmap for PVNH
normalize(saliency(loss_PVNH, seed_input=np.expand_dims(shuffled_val_X[i], axis=0), smooth_noise=0.2))
]
# Define figure
f=plt.figure(figsize=(20, 8))
# Define the image grid
grid = ImageGrid(f, 111,
nrows_ncols=(2, 3),
axes_pad=0.05,
share_all=True,
cbar_location="right",
cbar_mode=None,
cbar_size="2%",
cbar_pad=0.15)
# Iterate over the graphs
for j, axis in enumerate(grid):
# Plot original
im=axis.imshow(original_image)
im=axis.imshow(list_heatmaps[j][0], cmap='jet', alpha=0.5*valInceptionResNetV2[i][j%3])
im=axis.set_xticks([])
im=axis.set_yticks([])
# Create scalarmappable for obtaining the colorbar from 0 to 1
sm = plt.cm.ScalarMappable(cmap='jet', norm=plt.Normalize(vmin=0, vmax=1))
plt.colorbar(sm)
plt.show()
EACH ORIGINAL MRI IS ANALYZED WITH TWO METHODS: CLASS ACTIVATION MAP (UPPER ROW) AND SALIENCY MAP (LOWER ROW) EACH MAP IS SUPERIMPOSED ON THE ORIGINAL MRI WITH A TRANSPARENCY THAT IS INVERSELY PROPORTIONAL TO THE ESTIMATED PROBABILITY OF THE MRI BELONGING TO THAT CATEGORY (NORMAL MRI, DIFFUSE CORTICAL MALFORMATION, OR PERIVENTRICULAR NODULAR HETEROTOPIA) HIGHER ESTIMATED PROBABILITIES PRODUCE CLEARLY SEEN MAPS OVERLAID ON THE ORIGINAL MRI AND LOWER ESTIMATED PROBABILITIES PRODUCE VERY TRANSPARENT OR NOT APPRECIABLE MAPS OVERLAID ON THE ORIGINAL MRI REAL CLASSIFICATION OF THE IMAGE: PVNH MODEL CLASSIFICATION OF THE IMAGE: PVNH Prob. Normal MRI: 0.0001 Prob. Diffuse MCD: 0.0000 Prob. PVNH: 0.9999
REAL CLASSIFICATION OF THE IMAGE: PVNH MODEL CLASSIFICATION OF THE IMAGE: PVNH Prob. Normal MRI: 0.0135 Prob. Diffuse MCD: 0.0004 Prob. PVNH: 0.9862
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 0.9998 Prob. Diffuse MCD: 0.0001 Prob. PVNH: 0.0001
REAL CLASSIFICATION OF THE IMAGE: PVNH MODEL CLASSIFICATION OF THE IMAGE: PVNH Prob. Normal MRI: 0.0000 Prob. Diffuse MCD: 0.0000 Prob. PVNH: 1.0000
REAL CLASSIFICATION OF THE IMAGE: PVNH MODEL CLASSIFICATION OF THE IMAGE: PVNH Prob. Normal MRI: 0.0000 Prob. Diffuse MCD: 0.0000 Prob. PVNH: 1.0000
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 1.0000 Prob. Diffuse MCD: 0.0000 Prob. PVNH: 0.0000
REAL CLASSIFICATION OF THE IMAGE: Diffuse MCD MODEL CLASSIFICATION OF THE IMAGE: Diffuse MCD Prob. Normal MRI: 0.0032 Prob. Diffuse MCD: 0.9915 Prob. PVNH: 0.0053
REAL CLASSIFICATION OF THE IMAGE: Diffuse MCD MODEL CLASSIFICATION OF THE IMAGE: Diffuse MCD Prob. Normal MRI: 0.0000 Prob. Diffuse MCD: 1.0000 Prob. PVNH: 0.0000
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 1.0000 Prob. Diffuse MCD: 0.0000 Prob. PVNH: 0.0000
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 1.0000 Prob. Diffuse MCD: 0.0000 Prob. PVNH: 0.0000
REAL CLASSIFICATION OF THE IMAGE: PVNH MODEL CLASSIFICATION OF THE IMAGE: PVNH Prob. Normal MRI: 0.0001 Prob. Diffuse MCD: 0.0001 Prob. PVNH: 0.9998
REAL CLASSIFICATION OF THE IMAGE: PVNH MODEL CLASSIFICATION OF THE IMAGE: PVNH Prob. Normal MRI: 0.0001 Prob. Diffuse MCD: 0.0000 Prob. PVNH: 0.9999
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Diffuse MCD Prob. Normal MRI: 0.0252 Prob. Diffuse MCD: 0.9408 Prob. PVNH: 0.0340
REAL CLASSIFICATION OF THE IMAGE: Diffuse MCD MODEL CLASSIFICATION OF THE IMAGE: Diffuse MCD Prob. Normal MRI: 0.0224 Prob. Diffuse MCD: 0.9766 Prob. PVNH: 0.0010
REAL CLASSIFICATION OF THE IMAGE: Diffuse MCD MODEL CLASSIFICATION OF THE IMAGE: PVNH Prob. Normal MRI: 0.0001 Prob. Diffuse MCD: 0.0095 Prob. PVNH: 0.9905
REAL CLASSIFICATION OF THE IMAGE: Diffuse MCD MODEL CLASSIFICATION OF THE IMAGE: Diffuse MCD Prob. Normal MRI: 0.0000 Prob. Diffuse MCD: 1.0000 Prob. PVNH: 0.0000
REAL CLASSIFICATION OF THE IMAGE: Diffuse MCD MODEL CLASSIFICATION OF THE IMAGE: Diffuse MCD Prob. Normal MRI: 0.0000 Prob. Diffuse MCD: 1.0000 Prob. PVNH: 0.0000
REAL CLASSIFICATION OF THE IMAGE: Diffuse MCD MODEL CLASSIFICATION OF THE IMAGE: Diffuse MCD Prob. Normal MRI: 0.0000 Prob. Diffuse MCD: 0.7767 Prob. PVNH: 0.2233
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Diffuse MCD Prob. Normal MRI: 0.2934 Prob. Diffuse MCD: 0.6963 Prob. PVNH: 0.0102
REAL CLASSIFICATION OF THE IMAGE: Diffuse MCD MODEL CLASSIFICATION OF THE IMAGE: Diffuse MCD Prob. Normal MRI: 0.0000 Prob. Diffuse MCD: 1.0000 Prob. PVNH: 0.0000
